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<> machine learning (415)

A blindfold approach improves machine learning privacy

1 min read ·
A query-based method for extracting knowledge from sensitive datasets without showing any underlying private data could resolve long-standing privacy concerns in machine learning.

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.

Relations between Decision Trees and Decision Rule Systems

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B5 L5 R5220

This thesis investigates the complex transformation of decision rule systems into decision trees, establishing bounds on tree depth, developing polynomial-time path-finding algorithms and an optimal dynamic programming algorithm for minimum depth, and analyzing time-space complexity trade-offs for trees over infinite binary information systems.

On Complexity of Decision Trees for Decision Tables from Closed Classes

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B5 L5 R5220

This thesis considers classes of decision tables closed under operations of removal of attributes (columns) and changing of decisions and study for the tables from these classes relationships among the minimum complexity of deterministic and nondeterministic decision trees and the complexity of the set of attributes attached to columns of the table, while also analyzing the algorithms for constructing decision trees for these decision tables.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

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B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Towards Trustworthy News Recommendation Systems

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B3 L5 R5209

This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

Some Contributions to Particle and Unbiased Simulation Methods

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This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.

On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning

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B9 L2 R2325

This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.
Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

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Auditorium 0215 between B2 & B3

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

Spatial Dataflow is all you need!

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B9 L2 R2325

Jian Weng's research, inspired by spatial dataflow, proposes a unified hardware design and implementation framework that leverages a familiar software programming paradigm, enabling concise and efficient designs while significantly reducing code complexity and achieving near-handcrafted performance, with future applications in ML inference systems targeting memory bandwidth limitations.
Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov and Professor Mikhail Moshkov published by Springer

1 min read ·
New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.
KAUST-CEMSE-Public-Colloquium-Marco-Canini-Why-ML-Works-Its-Not-Magic

Why ML Works: It’s Not Magic, It’s Networked Systems Wizardry!

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Today’s machine learning (ML) solutions are achieving extraordinary success across diverse fields, thanks to their ability to learn complex models and deliver impressively accurate results. While the availability of big data is often credited as a driving force, the unsung hero behind ML’s rise is the rapid innovation in computing and software systems that make these breakthroughs possible and accessible.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Bio2Vec Kickoff Meeting

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Paris France

​​Research Scientist Senay Kafkas will be attending the Bio2Vec kickoff meeting in Paris, France.

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Prediction of active and inactive chemical compounds from high-throughput assays

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B5 L5 R5209

Abstract This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptor. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledge base, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

The Evolution of Machine Learning

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B4 B5 A0215

This talk presents an overview of recent methodological advances in developing item-based nearest-neighbor-based recommender systems that have substantially improved their performance. Biography George Karypis is a Distinguished McKnight University Professor and an ADC Chair of Digital Technology at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests span the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

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B1 B4 B4214

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

ML Hub Seminar Series | The Machine Learning (ML) Hub

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B9 H2 R2325

The Machine Learning Hub @ KAUST is designed to be the one-stop-shop for machine learning (ML) and artificial intelligence (AI) at KAUST. It is an informal forum for exchanging ideas in these areas, including (but not limited to) theoretical foundations, systems, tools, and applications. It will be providing several offerings to the KAUST community interested in ML and AI, including a regular seminar series where new research in the field is presented, an online social forum dedicated to AI and ML discussions, announcements, brainstorming, collaborations, and hands-on activities (e.g

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Convolutional Neural Networks on Graphs

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B9 H2

In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data.

Day 2 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

From Bioinformatics to Translational and Health Informatics: Whitening the Black Box

Prof. Yasser EL-Manzalawy, Information Sciences and Technology, Pennsylvania State University

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B9 H1

Machine learning has been extensively used in developing predictive models for a variety of bioinformatics tools. In many of these applications, to achieve the highest possible predictive performance, black box models (e.g., SVMs and Neural Networks) had been preferred over white box models (e.g., Decision Trees and Rule-based models). First, I argue that sacrificing interpretability for the sake of performance is a reasonable decision for many bioinformatics applications. However, I will demonstrate that uncareful utilization of black box models can lead to misinterpretations of the results.

Day 1 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

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B1 L4 R4214

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Traffic Monitoring and MAC-Layer Design for Future IoT Systems

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B1 L4 R4214

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

Wireless Network Intelligence @ the Edge

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B1 L3 R3119

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Seeing is Believing? Media Integrity in a Post-Truth World

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

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B9 L2 H1

The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

-

KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Visible Light Communications Technology and Machine Learning - 2020-10-20

Paul Anthony Haigh, Newcastle University, United Kingdom and Dr. Bo Tan, Tampere University, Finland

-

KAUST

In this webinar we will give an overview of several key technologies related to our special issues in Frontiers of Communications and Networks; (1) visible light communications (VLC) and (2) applications of machine learning in optical and wireless communication systems.

Challenges in Marine Autonomy

Stefan B. Williams, Head of School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney

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KAUST

Featured Sessions
For more information please visit the website

Stylistic and spatial disentanglement in GANs

-

KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

Prof. Lassi Roininen, Applied Mathematics, LUT University

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B1 L4 R4214

We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

High-dimensional Regression and Dictionary Learning: Some Recent Advances for Tensor Data

Prof. Waheed Bajwa, Electrical and Computer Engineering, Rutgers University-New Brunswick

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B1 L3 R3119

Data in many modern signal processing, machine learning, and statistics problems tend to have tensor (aka, multidimensional / multiway array) structure. While traditional approaches to processing of such data involve 'flattening' of data samples into vectors, it has long been realized that explicit exploitation of tensor structure of data can lead to improved performance. Recent years, in particular, have witnessed a flurry of research activity centered around development of computational algorithms for improved processing of tensor data.

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

-

KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

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B9 L2 R2322 H1

In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Wide Bandgap Semiconductor Device Design via Machine Learning

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B2 L5 R5220

This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.

Embedded and Cyber Connected Systems (ECCS) Workshop

Prof. Ahmed Eltawil, Prof. Charalambos Konstantinou, Prof. Khaled Nabil Salama

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B2 L5 R5220

The workshop aims to bring together experts to present their latest research efforts related to Embedded and Cyber Connected Systems architectures and platforms that can scale efficiently, as well as operate securely and resiliently to provide the necessary resources demanded by current and future network applications.

Machine learning in hardware via trained metasurface encoders: theory, design and applications

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B2 L5 R5209

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Parameter-Free Online Optimization: Past, Present, and Future

Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

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B9 L2 R2322

Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.

Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

Dr. Danesh Moradigaravand, Infectious Disease Epidemiology lab, BESE, KAUST

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B3 L5 R5209

In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.

The Wild West of NLP Modeling, Evaluation, and Documentation

Dr. Nazneen Rajani, Research Lead, Hugging Face, California

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B2 L5 R5220

Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Machine Learning for Biomedical Engineering: from Oncological Electroporation to Gesture Recognition

Luciano Tarricone, Department of Innovation Engineering, University of Salento

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B9 L2 H2

In this presentation an overview is given on the most important research activities performed at the EML2.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

Machine Learning for Scalable MIMO Communication Systems: From Theory to Real-World Demonstration

Dr. Ahmed Alkhateeb, Electrical Engineering , Arizona State University

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B9 L2 H1

Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

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B9

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Exploiting Data Sparsity in Matrix Algorithms for Adaptive Optics and Seismic Imaging

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B2 R5209;

This thesis addresses the exponential growth of experimental data and the resulting computational complexity seen in two major scientific applications, which account for most cycles consumed on today's supercomputers.

Physics-based Simulation in Visual Computing

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B9 L2 H2 H2

This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Universal light encoders: artificial intelligent optical hardware for real-time hyperspectral imaging and ultrasensitive detection

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B9 L2 H2

In this talk, I will review the fundamentals and application of this technology in various areas, ranging from the inverse design of ultra-flat optical components to HyplexTM, an innovative camera for acquiring and processing high-resolution hyperspectral videos in real-time at 30 frames per second.

Mitigating Bias in Predictions from Machine Learning Models

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B9 L2 H2 H2

The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).

Security and privacy in centralized and decentralized machine learning

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B4 L5 R5209

Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

FADO: A Federated Learning Attack and Defense Orchestrator

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B5 L5 R5209

Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Thoughts About Machine Learning - Week 7

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B9 LH1 R2322

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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B9 H1 R 2322

The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

-

B2 B3 A0215

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Building SANDS at KAUST

1 min read ·
​​​​​We live in a connected world where networked systems play an increasingly important role. These systems, which are the foundational pillars of our modern digital lives, are the result of some remarkable technological advancements and progress in computer science over the past three decades.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

1 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference

1 min read ·
Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.

Alumni Feature: Arturo Magana Mora

1 min read ·
CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

KAUST partners with McLaren Racing on R&D

1 min read ·
McLaren Racing and KAUST signed a five-year research and development (R&D) agreement focused on extreme performance technology. The partnership aims to advance research in the areas of computational fluid dynamics (CFD), machine learning, fuels and lubricants, advanced mathematics and sensors and electronics. "This partnership underlines our commitment to supporting global STEM advancements," said Jonathan Neale, chief operations officer of McLaren Group. "KAUST is a leading research and education institution in these fields and mirrors our core values. McLaren Racing is part of a world

Mona Alshahrani defends her thesis proposal

1 min read ·
Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 2018.

Computing solutions for biological problems

1 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

Student Focus: Adel Bibi

1 min read ·
KAUST Ph.D. student Adel Bibi, who works on bridging the gap between theory and practice in deep learning, believes we're living in a very exciting time.

Putting the sense in materials

1 min read ·
An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

1 min read ·
KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

Flattening the sentimental curve

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-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

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A layer-based approach raises the efficiency of training artificial intelligence models.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

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Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

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Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

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Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

KAUST Prospective Student: Damilya Saduakhas

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Damilya Saduakhas, 21 years old, from Kazakhstan, obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Damilya has been selected as a young scientist to participate in the summer Data Lab from Yessenov Foundation and will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group under the supervision of Professor David Bolin.

Meet KAUST Prospective Student: Igor Sokolov

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Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

KAUST prospective student: Xiaochuan Gou

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Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST prospective student: Qizhou Wang

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Qizhou Wang, 22, is a communication engineering graduate who will join KAUST from the University of Electronic Science and Technology, China. Wang will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate in the KAUST Primalight Lab under the supervision of Professor Andrea Fratalocchi.

Meet KAUST prospective student: Zhenwen Liang

1 min read ·
Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST prospective student: Pratik Nag

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Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Computers excel in chemistry class

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Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Meet KAUST prospective student: Kai Yi

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Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Meet KAUST prospective student: Sahar Ammar

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Sahar Ammar will join KAUST this fall as a M.S./Ph.D. candidate and member of the Networking Research Lab and Communication Theory Lab under the co-supervision of Professor Basem Shihada and Professor Mohamed-Slim Alouini. The multidisciplinary engineering graduate will join the University from Tunisia Polytechnic School (TPS).

Meet KAUST student: Konstantin Burlachenko

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Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Reem Alghamdi

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Reem Alghamdi is a computer science graduate from Princess Nourah bint Abdulrahman University, Saudi Arabia. Reem will join KAUST this fall as a M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST prospective student: Waleed Abu Laban

1 min read ·
Waleed Abu Laban is an electrical engineering graduate who will join KAUST this fall as a M.S./Ph.D. candidate and member of the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

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The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Deep learning in the emergency department

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Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

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Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Machine Learning Arabic NLP webinar

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Join us to learn, compete, and win at the Machine Learning Webinar and learn more about participating in the Arabic NLP challenge! Register Now!

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

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KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Broadening horizons for people with quadriplegia

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An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Xin Gao promoted to rank of professor

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Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

Brain-on-a-chip would need little training

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A neural network that mimics the biology of the brain can be loaded onto a microchip for faster and more efficient artificial intelligence.

Machine learning at speed

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Optimizing network communication accelerates training in large-scale machine-learning models.

Training an AI eye on the Moon

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Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

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Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

Drop the zeros for a performance boost

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An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

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KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.

Deeper defense against cyber attacks

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To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Professor Eltawil officially inducted as a Senior Member of the National Academy of Inventors

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Earlier this year, KAUST Professor of Electrical Engineering Ahmed Eltawil was elected as a Senior Member of the National Academy of Inventors (NAI). Eltawil was officially inducted as a Senior Member at the recent NAI 10th Annual Meeting held in Florida, U.S.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

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KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Can machine learning help predict disease spread?

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Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

MS Thesis Defense: Rare variant collapsing analysis on UK Biobank by Yang Liu

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Yang Liu, M.S./Ph.D. candidate in the Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended her thesis "Rare variant collapsing analysis on UK Biobank".

MS Thesis Defense: Description Logic EL++Embeddings with Intersectional Closure by Xi Peng

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Xi Peng MS Student under the supervision of Professor Robert Hoehndorf successfully defended his thesis "Description Logic EL++Embeddings with Intersectional Closure"

Computer learns to write captions with feeling

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Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

AI helps diagnose post-COVID lung problems

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A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

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KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

Extreme analysis of seizures

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CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Steering Customized AI Architectures for HPC Scientific Workloads: A Bird of Feather at SC22!

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Abstract: We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems. We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid

BORG team win at the KAUST-NVIDIA GPU Hackathon 2022

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Congratulations to Senay Kafkas, Maxat Kulmanov and Fernando Zhapa Camacho of the Bio-Ontology Research Group (BORG) on winning at the KAUST-NVIDIA GPU Hackathon 2022. The 8th edition of the Hackathon on Accelerating Scientific Applications using Graphical Processing Units (GPUs) was held at King Abdullah University of Science and Technology (KAUST), twelve teams of students and researchers enjoyed the opportunity to accelerate their codes in GPU’s under the guidance of expert mentors of NVIDIA, Codee, KAUST Supercomputing Lab (KSL) and KAUST core labs.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

CEMSE New Faculty: Professor Rolf Krause

1 min read ·
Professor Rolf Krause, our new faculty member at KAUST, drives sustainable progress across various domains, from medicine to geology, with his pioneering research in applied mathematics and efficient algorithms.

Zihang Xiang

Ph.D. Student, Computer Science
Zihang Xiang is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Z. Xiang, M. M. Khan, U. Khalid and J. Xu, ”A DC link capacitors’ voltage balancing method in unidirectional cascaded H-bridge converter using multi-limb-core (MLC) transformers,” 2017 China International Electrical and Energy Conference (CIEEC),Beijing, 2017, pp. 606-611. U. Khalid, M. M. Khan, Z. Xiang and Y. Jianyang, ”Bidirectional modular Dual Active Bridge (DAB) converter

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Yuxi Hong

Ph.D. Student, Computer Science
Yuxi Hong is a PhD student working with Professor David Keyes. He is working on accelerating the training process of deep learning from different aspects. Why KAUST? KAUST is a great place for research. Why HPC and machine learning? I believe we need more computation in the future. Scalable and efficient computing methods are basic tools for the scientific research. Education Profile MS Tsinghua University. BS Tsinghua University.

Zainab A. Homoud

M.S. Student, Bioengineering
Research Interest Studying : Brain time series, fMRI, EEG, Signal Processing and Machine Learning for better Mental Disease Diagnosis and Consciousness Study. Previous Education Undergraduate study (B.S) in Biomedical Engineering and Health Science at the Joint program of North Carolina State University and UNC Chapel Hill.

Yibo Yang

Research Scientist
Yibo Yang is a research scientist at the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, he obtained his Ph.D. from Peking University in 2021. He worked as a researcher at JD Explore Academy, JD.com, in 2021-2023.

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Yuchen Li

Ph.D. Student, Computer Science
Yuchen Li is a PhD Student under the supervision of Professor Mohamed Elhoseiny in Vision-CAIR Group at Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before joining KAUST, Yuchen was a research intern at iFLYTEK, an intelligent speech and artificial intelligence company in Hefei, China, for three months. He was a Rocket MQ open source contributor and Alibaba summer of code student developer with rich research and engineering experience. He visited NUS, UBC, THU, and other prestigious universities for academic exchanges with a

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. student under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST). Publications Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. "Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits." https://arxiv.org/abs/2106.02575. (Equal contributions) Education Profile M.S. in Statistics, East China Normal University, China B.S. in Mathematics and Applied Mathematics, East China Normal University, China

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Xun Qian

Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected

Xiangyu Cui

I will enjoy the real fun of scientific research in KAUST. There are still many people who don’t have good access to internet.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaochuan Gou

Ph.D. Student, Computer Science
Xiaochuan Gou is a Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group (MINE) under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Xiaochuan obtained his master’s degree in Computer Science from National Chiao Tung University and a bachelor’s degree in Spatial Information and Digital Technology from Wuhan University, China, respectively. Research Interests Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic

Xi Peng

Ph.D. Student, Computer Science
Xi Peng likes pop music and doing sports. He graduated from SUSTech in 2021 and planning to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf. "He says KAUST has the best environment to do research. Life in KAUST is also comfortable. I believe I can achieve a lot in KAUST". Research Interest applications of machine learning and logic integration in biology Education Profile B.Sc, Computer Science, Southern University of Science and Technology, Shenzhen, China, 2017-2021 M.Sc, Computer Science, King Abdullah University of Science and Technology, Thuwal

Waqas Waseem Ahmed

Erasmus Mundus Joint Doctorate in Photonics Engineering, Universitat Politècnica de Catalunya (Spain), Universita degli Studi di Firenze (Italy), Karlsruhe Institute of Technology (Germany), Université Paul Cèzanne Aix Marseille III (France).(2018) MS in Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. (2014) BS in Electronic Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Pakistan. (2009) Research Interests Non-Hermitian Photonics, Nonlinear Optics, Machine Learning, Laser Physics.

Waleed Abu Laban

Waleed Abu Laban is a M.S./Ph.D. candidate at the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil. Before joining KAUST, Waleed obtained a bachelor's degree in electrical engineering from The University of Jordan. Research Interests Waleed's research interests include signal processing and machine learning.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Ussama Zahid

Ussama Zahid was an M.S. student in the Accelerated Connected Computing Lab at KAUST. He works at the intersection of deep learning and embedded systems, i.e., “Edge AI”. He specializes in efficient training and inference of quantized deep neural networks (DNNs) and hardware-software co-design of DNN accelerators. Before joining KAUST, he was as a research engineer intern at Xilinx Research Labs, Dublin Ireland, Ireland where he worked on quantized and robust training of Deep Neural Networks (DNNs), resulting in patents and publications. He received his Bachelor's degree in Electrical

Tarek H. Mahmoud

Areas of Expertise and Research Interests ​Computer Vision Machine Learning Robotics Education Profile ​MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) ​​BS in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia (2016)

Tilman Hinnerichs

Visiting Student, Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Talha Ariff

Talha is studying the use of novel fibre-optic distributed acoustic sensing (DAS) as a tool to help monitor and control anthropogenic acoustic pollution in marine environments.

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Slavomir Hanzely

Slavomir Hanzely is an MS/Ph.D. student in the Applied Mathematics and Computational Science Program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Slavomir earned his Bachelor's Degree in Informatics with a background in both Mathematics and Computer Science from the Faculty of Mathematics, Physics and Informatics University (FMPH UK), Slovakia in 2019. Research Interest He is interested in Machine Learning, Federated Learning, and Stochastic Algorithms. Education

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shahid Hussain

Ph.D. Student, Computer Science
Research Interests Design and analysis of decision trees based on dynamic programming and greedy approaches Machine learning Discrete optimization Selected Publications Azad, M., Chikalov, I., Hussain, S., and Moshkov, M. Restricted multi-pruning of decision trees, In Machine Learning and Data Analytics Symposium as plenary talk (2016) (Awarded best paper). Busbait, M., Chikalov, I., Hussain, S., and Moshkov, M. Diagnosis of constant faults in read-once contact networks over finite bases. Discrete Applied Mathematics, 184 (2015), pp. 76–88. AbouEisha, H., Hussain, S., Lozin, V., Monnot, J

Shahzad Gishkori

Postdoctoral Research Fellow (former), Information Science Lab
Postdoctoral Research Fellow, Electrical and Computer Engineering , King Abdullah University of Science and Technology.

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Shasha Liu

Shasha Liu is a visit student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shasha Liu will obtain her bachelor degree in Information Countermeasure Technology from University of Electronic Science and Technology of China (UESTC) in 2022. Scientific Interest Shasha Liu is interested in Machine Learning and Communicate Engineering. Education Profile B.S. in Information Countermeasure Technology, University of Electronic Science and Technology of China (UESTC)

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Salman Alsubaihi

Salman Alsubaihi was an Electrical and Computer Engineering Master Student in the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Salman Alsubaihi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Saudi Arabia in 2017. In 2019, he received his Master in Electrical Engineering from KAUST. Research Interests ​ Salman is focusing on understanding deep neural

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling
Sameh Abdulah

Sameh Abdulah

Sameh Abdulah > Appointments, Roles and Affiliations, Hierarchical Computations on Manycore Architectures
Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.

Ruslan Zhagypar

MS/PhD Student, Electrical and Computer Engineering , King Abdullah University of Science and Technology

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Reema Alnafisi

M.S. student, advised by Professor Tareq Al-Naffouri under the Information System Lab (ISL). at King Abdullah University of Science and Technology (KAUST)

Ricardo Henao

Associate Professor (former), Bioengineering
​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering ( BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division ( CEMSE), member of the Smart Health Initiative ( SHI) and the Computational Biosciences Research center ( CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering ( ECE)

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzan Umarov

Ph.D. Student, Computer Science
Ramzan is a PhD candidate, working on solving biological problems by using applied machine learning with focus on deep learning. He has developed deep learning based methods to solve bioinformatics problems achieving state-of-the-art performance, focusing on various aspects of gene regulation. Umarov obtained his Master degree from Imperial College London, Advanced Computing course. Research Interests The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning. Professional Profile 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA 2013-2014, Programmer

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Rahaf Shiqdar

Visiting Student, Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Raphael Bridi

Ph.D. Student, Electrical and Computer Engineering ,King Abdullah University of Science and Technology (KAUST).

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Qizhou Wang

Qizhou Wang is an M.S./Ph.D. candidate in the KAUST Primalight research group under the supervision of Professor Andrea Fratalocchi. Before joining KAUST, Qizhou obtained a bachelor's degree in communication engineering from the University of Electronic Science and Technology, China. Research Interests Qizhou's research interests include machine learning, optimization theories and computer vision.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Peihao Li

Peihao Li is a Ph.D student in Electrical Engineering department within CEMSE division. He got a B.S in Observation, Navigation and Control from Northwestern Polytechnical University, China. He received master degree in the Ms/PhD program in Electrical Engineering at King Abdullah University of Science and Technology (KAUST) in winter 2017. His research interests include UAV navigating, image processing and robust control. Currently, he is working on developing algorithms and applications in signal processing and is especially interested in semi-classical signal analysis (SCSA). Research

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Olga Mashkova

Ph.D. Student, Computer Science
PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Norah S. Alballa

Ph.D. Student, Computer Science
Education Profile Ph.D. in Computer Science, CEMSE, KAUST, 2022-present M.S. in Computer Science, Portland State University, 2016 B.S. in Information Technology, King Saud University (KSU), 2010

Mohammed Hussain AlSharif

Ph.D. Student (former), Electrical and Computer Engineering
Ph.D. Degree, Electrical and Computer Engineering ,King Abdullah University of Science and Technology, KAUST,
Mostafa Emara

Mostafa Mohamed Lotfy Emara

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
PhD Student, Electrical and Computer Engineering at KAUST (King Abdullah University of Science and Technology)

Mudassir Masood

Ph.D. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST). ,Assistant Professor, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Motasem Alfarra

Motasem Alfarra is an Electrical and Computer Engineering Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Mohammed Aldosari

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2018.08 - 2020.06 M.S. in Mechanical Engineering, KAUST, KSA 2011.08 - 2016.05 B.Sc. in Aerospace Engineering, KFUPM, KSA Professional Profile 2022.01 - 2022.05 Teaching Assistant, CEMSE, KAUST, KSA 2017.07 - Present Researcher, Center of Excellence for Aeronautics & Astronautics, King Abdulaziz City for Science and Technology, KSA 2017.04 - 2017.07 Maintenance Planning Engineer, SaudiGulf Airlines, KSA Selected Publications M.N.Aldosari, K.K.Yalamanchi, X. Gao, S.M. Sarathy, "Predicting entropy and heat capacity

Mohammed Aasim Shaikh

MS Degree ,Electrical and Computer Engineering ,King Abdullah University of Science and Technology (KAUST) .

Mohammed Ali Abugurain

Visiting student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Mohammad Tamim Alkhodary

Ph.D. degree in electrical engineering from the King Fahd University of Petroleum and Minerals. Postdoctoral Fellow at École de technologie supérieure, Montreal, QC, Canada

Mohammad Shehab

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education and Early Career Currently, Mohammad Shehab is a postdoctoral Researcher with CEMSE Division, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Previously, he was with the University of Oulu, Finland, from which he obtained his doctoral degree in 2022. Prior to that, he obtained his B.Sc from Alexandria University in 2011, two M. Sc degrees from the Arab Academy and University of Oulu in 2014 and 2017, respectively. He worked as TA at Alexandria University and the Arab Academy in Egypt from 2012-2015. Shehab was a visiting researcher to TU Dresden, Aalborg

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Mohamed A Suliman

Visiting Scholar (former), Information Science Lab
MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London..

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Mher Safaryan

Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Matthias Mueller

Matthias Mueller was an Electrical and Computer Engineering Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at Visual Computing Center (VCC) under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Matthias graduated with a B.Sc. in Electrical Engineering and Math Minor from Texas A&M University in 2011. After graduation, he joined P+Z Engineering in Munich, Germany as an Electrical Engineer and worked for 3 years on the development of mild-hybrid electric machines at BMW. In 2014, he started his M

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 IT Specialist with a background in quantum engineering and data science. He leverages analytical, multidisciplinary, and data-driven approaches to drive innovation and efficiency within the IT infrastructure. He excels at full-stack development, identifying complex problems' root causes and developing impactful long-term solutions within a broad area of competence.

Maria Sara Nour SADOUN

Visiting Student (former), Information Science Lab
Research internship in King Abdullah University of Science and Technology within EMANG as a Visiting Student on the topic of Epileptic Seizure Detection using Electroencephalography (EEG).

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Mamdouh AlJoud

Intern (former), Information Science Lab
Research Intern at King Abdullah University of Science and Technology (KAUS)

Manal A. Alshehri

Ph.D. Student, Computer Science

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Lin Chen

Lin is a visiting student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST) Education and Early Career Lin is a 3rd-year bachelor student in Communication Engineering from University of Electronic Science and Technology of China (UESTC). Research Interest Lin is interested in enhancing the performance of the communication system to meet the communication requirements of the evolving new application scenarios. She’s currently exploring the application of RIS and artificial intelligence in

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Lijie Hu

Ph.D. Student, Computer Science
Lijie Hu is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". https://arxiv.org/abs/2107.11136. To appear in the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022). Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan and Jinhui Xu. "Differentially Private Expectation

Lizhong Ding

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Lizhong Ding worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Lizhong is a research scientist at the Inception Institute of Artificial Intelligence. Research Interests Machine learning theory Impact of approximation on learning algorithms, especially discrepancy bounds between approximate and accurate learning, for kernel learning and deep learning Large scale machine learning Large scale matrix analysis/approximation Scalable model

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Li Zhou

Ms/PhD Student, CTF player, Interested in Reverse Engineering

Lama Burhan Niyazi

Ph.D. degree in the Communication Theory Lab (CTL) under the joint supervision of Professor Mohamed-Slim Alouini and Professor Tareq Al-Naffouri , King Abdullah University of Science and Technology (KAUST)

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Kuat Telegenov

An experienced Robotics Research Engineer and Laboratory Manager.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering
PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Kerven Durdymyradov

Ph.D. Student, Computer Science
Honors and Awards Bronze Medal and Honourable Mention- International Mathematical Olympiad (IMO). Netherlands–Kazakhstan (2011, 2010) 1st and 2nd place Winner - Open Doors International Olympiad - Mathematics & AI track. Russia (2022, 2020) Silver and Bronze Medals - Balkan Mathematical Olympiad (BMO). Romania-Moldova (2011, 2010) Silver and Bronze Medals - South Eastern Mathematical Olympiad (SEEMOUS). Romania-Greece (2014, 2013)

Kamilya Smagulova

Postdoctoral Research Fellow, Communication and Computing Systems Lab
Education and early career Kamilya received her bachelor degree in Radio Engineering and Telecommunications from Almaty University of Power Engineering and Telecommunications (2008) and masters degree in Nanotechnology from University College London (2011). In 2021 she obtained her PhD degree in Electrical and Computer Engineering from Nazarbayev University. Currently Kamilya is a postdoctoral fellow at Communication and Computing Systems Lab (CCSL) under supervision of Prof. Ahmed Eltawil. Her research interests lie primarily in the area of machine learning and resistive hardware accelerators

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Jürgen Schmidhuber

Professor, Computer Science
Professor Schmidhuber is a renowned computer scientist, best known for his pioneering work in artificial intelligence (AI), deep learning and artificial neural networks. He has authored more than 350 peer-reviewed papers and has provided AI strategies to several governments.

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Juncai He

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application

Jose Roberto Ayala Solares

Jose Roberto Ayala Solares was a CTL lab member and graduated from KAUST. Then, he joined the Monterrey Institute of Technology and Higher Education as a lecturer. Selected Publications M.S. Thesis: J. R. Ayala Solares, “Optimal Power Allocation of a Sensor Node under Different Rate Constraints,” King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, Jul. 2011. Conference Publications: J. R. Ayala Solares, Z. Rezki, and M. -S. Alouini, "Optimal power allocation of a sensor node under different rate constraints," in Proc. of IEEE International Conference on

José Maria Sosa Gómez

PhD student at KAUST leveraging machine learning and radar systems to enhance patient procedures, under the guidance of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL).

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Jiayao Zhang

M.S. Student, Computer Science
Jiayao Zhang is a Computer Science Master's student in the MS/Ph.D. program under the supervision of Professor Dominik Michels at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Jiayao completed her Bachelor's degree from the Chongqing University of Posts and Telecommunications. She was working with Prof. Chenqiang Gao on Event Detection and recognition topics. She was a visiting student at Shanghai Jiao Tong University (SJTU) in China with Prof. Cewu Lu in the MVIG lab and also worked as a Research

Jia-Hong Huang

Jia-Hong Huang was a Master's student in the Electrical and Computer Engineering Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Career Jia-Hong earned a double major bachelor's degree in Statistics and Mathematical Science from National Chengchi University in Taiwan in 2014. He received his master's degree in Mechanical Engineering from National Taiwan University in 2016. After that, he joined KAUST and received his second master's degree in Electrical Engineering in 2018

Jay Morgan

Visiting Student, Computer Science
Jay Morgan was a visiting student at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. ​Research Interests Jay's research is focused on the verification of machine learning (ML) models, in particular, deep learning. Reasoning as to the decision process of ML is becoming increasingly important especially those that are included in safety-critical systems. Therefore, verifying the properties of the model against requirements as well as interrogating the model itself could provide a great benefit.​​ His

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Ismail Ben Atitallah

Ismail Ben Atitallah was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Islam Ashry

Senior Research Scientist, Electrical and Computer Engineering
Senior Research Scientist with over 10 years of experience in optics, specializing in developing optical sensors as real-world solutions for underwater exploration, smart agriculture, and the oil and gas industry. Senior Member of the National Academy of Inventors (NAI), IEEE, and IEEE Photonics Society.

Israel Jesus Santos Filho

MS Student, Electrical and Computer Engineering ,work under the supervision of Professor Tareq Al-Naffouri at King Abdullah University of Science and Technology.

Ismail AlQerm

Ismail AlQerm obtained his Ph.D. degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Currently, he is working as a Post-Doctorate Fellow at the University of Missouri-St. Louis, USA. Research Interests Ismail's research interests included Cognitive radios, Resource Allocation, Machine Learning, Cellular Networks, and HetNets. Thesis title: Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks. Academic Advisors Basem Shihada, Professor, Computer

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Imane Boudellioua

Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory

Igor Chikalov

Senior Research Scientist, Computer Science
Research Interests Application of dynamic programming to building decision trees and decision rules Machine learning Design of algorithms and complexity analysis Selected Publications I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H.S. Nguyen, A. Skowron, B. Zielosko, Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical Analysis of Data, Intelligent Systems Reference Library, Vol. 41, Springer, 2012 I. Chikalov, Average Time Complexity of Decision Trees, Intelligent Systems Reference Library, Vol. 21, Springer, 2011. I. Chikalov, P. Yao, M. Moshkov, J.C. Latombe, Learning

Hussam Wael Ismail Ibraiwish

Hussam Ibraiwish is a Master’s student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hussam has obtained his Bachelor’s degree in Electrical Engineering from the University of Jordan in 2021. He was a visiting student at CTL in 2020 before becoming a KAUST student in 2021. Hussam was also the Vice Chair of the IEEE ComSoc in the University of Jordan. Research Interest Hussam is interested in machine learning and communication theory. He is particularly

Ibrahim Alshehri

intern at King Abdullah University of Science and Technology(KAUST)

Ibrahim Abdelaziz

Senior Research Scientist, IBM
Selected Publications Using Advanced Hidden Markov Models for online handwriting recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy In Proceeding of Asian Conference of Pattern Recognition (ACPR), (2011) Large Vocabulary Arabic Online Handwriting Recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy International Journal on Document Analysis and Recognition, (2014) Artificial Tutor for Arabic Handwriting Training. Sherif Abdou, Aly Fahmy, Ibrahim Hosny, Eman Mostafa In Proceedings of the Second International Conference on Arabic Language Resource, (2009) Education Profile 2013 - 2018: PhD

Humam Alwassel

Ph.D. Student, Computer Science
Humam Alwassel is a Computer Science Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor degree with double major in Computer Science and Mathematics from Cornell University in New York, USA in 2016. After that, he joined KAUST for the MS/PhD program and received his master degree in Computer Science in 2018. Research Interest He’s currently focused on the development of novel computer vision techniques for video

Hasan Abed Al Kader Hammoud

Hasan is an Electrical and Computer Engineering MS/Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Hasan obtained his bachelor's degree in Electrical and Computer Engineering from American University of Beirut in 2020. He joined KAUST in 2020 to pursue his MS and PhD degrees. Research Interest Deep Learning Machine Learning Computer Vision Publications Rapid Adaptation in Online Continual Learning: Are We

Hajer Abbas

Visiting Student (former), Electrical and Computer Engineering
Visiting Student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Guang An Ooi

Research Interests Guang develops systems for real-time wellbore casing inspection and 3D visualization. He constructs novel neural networks for electromagnetic inspection tools developed in conjunction with the MERGE team, which generates casing cross-sectional images from spatio-temporal data. In addition, Guang is interested in programming drilling simulators in Java and Python. Education Profile B.Sc., Aeronautics and Astronautics Engineering, National Cheng Kung University, Taiwan, 2017 M.Sc., Electrical Engineering, King Abdullah University of Science and Technology, Saudi Arabia, 2019

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Fuad Jamour

Fuad Jamour is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Fuad's research interests include Data Management and Machine Learning. Education Profile ​B.Sc. of Computer Engineering, University of Jordan, Amman, Jordan.

Francesco Orabona

Associate Professor, Computer Science
Achieving "parameter-free" machine learning is the primary focus of Professor Francesco Orabona. He is particularly interested in designing superior algorithms to train deep learning models.

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Fares Fourati has studied mathematics and physics at the University of Sfax and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2017. In 2020, he received his diplôme d’ingénieur with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. In 2022, he received his Master of Science in electrical and computer engineering from KAUST. Early Career Before

Fat-hy Omar Rajab

Master's Thesis Abstract The multi-agent robotic system has been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased efficiency in multi-agent systems is achieved by the execution of the task in a cooperative manner. But to achieve cooperation in multi-agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent systems rely on the use of the GPS to provide global positioning information which suffers great deterioration in performance in indoor applications, and also

Fabian Heilbron

Fabian Caba Heilbron was a Ph.D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). He is currently a Research Scientist at Adobe Research in San Jose, California. Education and Early Career Fabian has a Bachelor’s Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia, 2011. In 2013, he received his Master Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia. He joined KAUST as a Ph.D. candidate and received his

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Faisal Mushayt

M.S. Student, Computer Science
Education 2020.09 - Present M.S. in Computer Science, King Abdullah University of Science and Technology, KSA 2016.09 - 2020.08 B.S. in Computer Science, Boston University, USA Professional Profile 2018.06 - 2018.09 Data Scientist Intern, School of Business, LISH, Harvard, USA 2017.09 - 2018.05 Teaching Assistant, Math & Computer Science, Boston University, USA Areas of Expertise and Research Interests ​Machine Learning Learning in Physical Environments AI & Robotics Safety Engineering

Enas M. Odat

Enas Odat received the B.S. degree in computer engineering from the Jordan University of Science and Technology in 2009, and the M.S. degree from the Department of Computer Science, King Abdullah University of Science and Technology, in 2011, where she is currently pursuing the Ph.D. degree. Her research interests include machine learning, game theory, and Cyber-Physical systems. Education Profile MS. Computer Sciences, King Abdullah University of Sciences and Technology (KAUST), 2011 BS. Computer Engineering, Jordan University of Science and Technology (JUST), 2009

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Elsiddig Awadelkarim Elsiddig

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Emaad Ahmed Manzoor

Emaad Ahmed Manzoor obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interests Emaad's research interests included Diffusion of information, influence in networks, Algorithms for machine learning and optimization on graphs. Education Profile MS Computer Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2013-2015). BE Computer Science, BITS - Pilani Goa Campus, India. (2008 - 2012) Professional

Ebrahim Al-Safadi

Visiting B.S. Student, Information Science Lab
Internship at King Abdullah University of Science and Technology (KAUST).

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Djidenou Montcho

Ph.D. Student, Statistics
Djidenou (Hans) Montcho is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Hans obtained his Bachelor Degree in Applied Mathematics at the Federal University of Rio Grande in Brazil. He graduated with a Master of Science from the Federal University of Sao Carlos and the University of Sao Paulo. He has worked as a data scientist for a large credit bureau in Brazil. Research Interest His research interest is mainly in Bayesian and

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

David Keyes

Senior Associate to the President, King Abdullah University of Science and Technology
David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Cian Leo Blake

Cian is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Cian obtained BSc in Physics with a Computer Science minor from New York University Abu Dhabi (NYUAD) in 2023. Alongside his studies, Cian founded an autotech startup called Genius Autos (www.genius-autos.com) that aims to optimize the global used auto parts supply chain in the age of artificial intelligence. Research Interest Cian is focusing in the area of wireless communication

Cheng-Long Wang

Ph.D. Student, Computer Science
Cheng-Long Wang is currently a third-year Ph.D. student at ‌Provable Responsible AI and Data Analytics (PRADA) Lab under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). His research is on machine unlearning, aiming to bridge the gap between personal/organizational data protection requirements and digital-world workflows.

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Biao Zhang

Ph.D. Student, Computer Science
Biao Zhang is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. Education and Early Career Biao Zhang obtained his bachelor degree in Mathematics and master degree in Applied Mathematics from Xi’an Jiaotong University in China. Research Interest Biao Zhang is mainly interested in machine learning and computer vision. Education Profile M.S., Applied Mathematics, Xi’an Jiaotong University, China B.S., Mathematics, Xi’an Jiaotong University

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Basit Olakunle Alawode

Visiting student at King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq AlNaffouri.

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Asma Abdullah Sarouji

Visiting Student (former), Electrical and Computer Engineering
Research intern at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Tareq Al-Naffouri on health project that has a significant impact on people’s lives.

Ayan Shymyrbay

Ayan Shymyrbay is a Ph.D. candidate in the electrical and computer engineering program at the King Abdullah University of Science and Technology (KAUST).

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Ashraf M. Kibriya

Research Interests ​My research interests include the application of Big Data and Machine Learning techniques to real-world problems in various domains, including biology and biomedicine. In particular, I'm interested in Deep Learning, Text Mining, Network Analysis and Graph Mining. Professional Profile ​2018-Present: Postdoctoral Fellow, KAUST, Thuwal, Saudi Arabia 2011-2018: Doctoral Fellow, KU Leuven, Belgium 2010-2011: Pre-doctoral fellow, KU Leuven, Belgium 2007-2009: Database developer / Datamining specialist, Datamine Ltd., New Zealand 2002-2005: Research Programmer, Machine Learning

Azimkhon Ostonov

Ph.D. Student, Computer Science

Asaad S. Alghamdi

Arman Ibrayeva

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student, KAUST, KSA 2018.08 - 2020.07 M.S. in Mechanics, Kazakh National University, Kazakhstan 2014.08 - 2018.07 B.S. in Mechanics, Kazakh National University, Kazakhstan Professional Profile 2020.03 - 2021.11 Researcher, Institute of Mechanics and Machine Sciences, National Academy of Sciences, Kazakhstan 2019.06 - 2021.11 Engineer, KOLSAJ STROJ, Kazakhstan 2014.05 - 2015.05 Laboratory assistant, Fluid and Gas Mechanics Lab, Kazakh National University, Kazakhstan Selected Publications S. Ibrayev, N. Jamalov, A. Ibrayeva, G. Mukhambetkaliyeva "Optimal

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals

Aritra Dutta

Aritra Dutta is a Postdoctoral Research Fellow at the Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to that, he was a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Aritra Dutta received his bachelor degree in Mathematics (Hons.), Minor in Physics and Statistics from Presidency College in Calcutta University. He obtained a master degree in Mathematics and Computing from Indian

Amine Bejaoui

Amine Bejaoui was an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amine Bejaoui obtained his engineering degree with majors in Economics and Scientific management from Ecole Polytechnique de Tunisie (EPT) , Tunisia, in 2015. Research Interest The scientific interests of Amine Bejaoui lies in the area of statistics, machine learning and the applications of Random Matrix Theory. Education Profile M.Sc. in Statistics, KAUST, Aug 2018 – Present

Amin Almozel

Education 2021.01 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA 2018.08 - 2019.12 M.S. in Electrical and Computer Engineering, KAUST, KSA 2014.08 - 2018.05 B.S. in Electrical and Computer Engineering, University of Rochester, USA Selected Publications A. Almozel, J.S. Shamma, "Competitive Drone Racing Using Asymmetric Games" IEEE 18th International Conference on Ubiquitous Robots (UR), 349-356, 8 (2021). DOI: 10.1109/UR52253.2021.9494629 Areas of Expertise and Research Interests Autonomous and Intelligent Systems​

Amanat Kafizov

Education & Early Career Amanat was graduated from Nazarbayev University in 2019 where he received a Bachelor of Engineering in Electrical and Electronic Engineering. Amanat was a participant of Erasmus+ student exchange program. During this program, he studied fall semester 2018 at Ecole Centrale de Nantes university as a Foundation Master student in Control, Robotics and Signals. Research Interest Amanat did a summer research internship 2018 at University at Buffalo, New York, USA. He was working on Terahertz (THz) communication networks under a supervision of professor Josep Jornet. To be

Alyazeed Basyoni

M.S. Student, Computer Science
Alyazeed Basyoni is a CS Master Student under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alyazeed joined Carnegie Mellon University in Pittsburgh, Pennsylvania and earned his bachelor degree in Computer Science with minor in Mathematics in 2018. Education Profile B.S., Computer Science, Carnegie Mellon University, USA, 2018

Alya AlShaikh

Alya AlShaikh is currently an undergraduate student at Alfaisal University in Riyadh, KSA. She is pursuing a Software Engineering BSc specializing in Cyber Security and Machine Learning. Alya joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Konstantinou as an Accelerated Bachelor’s and Master's (ABM) student. Her research interests include applying machine learning techniques to enhance cyber defense strategies.

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.

Alam Zaib

Research Intern at King Abdullah University of Science and Technology (KAUST) ,Assistant Professor / COMSATS institute of information technology, Abbottabad.

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).

Ahmed Mohamed Abdelmoniem Sayed

Dr. Ahmed Sayed is a Postdoctoral research fellow at ECRC, working with Professor Marco Canini. He is focused on tackling challenging problems related mainly to improve the performance and understanding of machine learning systems and their usage for optimizing and automating computer and network systems. Why KAUST? KAUST is the beacon for innovation and creativity in the middle-east area and they follow the model of HKUST to become a world-wide top-tier leading university in science and technology. Why networking The intersection point between Networked Systems & Machine Learning is a very

Ahmed Douik

Visiting B.S. Student, Computer, Electrical and Mathematical Sciences and Engineering , Quantitative Researcher at Chicago Trading Company PhD candidate at California Institute of Technology

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies

Abeer Almutari

M.S. Student, Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdulrahman Alanazi

Visiting Scholar (former), Information Science Lab
Visiting Scholar, Information Science Lab at Computer, Electrical and Mathematical Science and Engineering (CEMSE) , Assistant Professor at King Saud University

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in

Abdullah Ahmed Zayat

Visiting Student Research Program under the supervision of Professor Tareq Al-Naffouri ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdullah Al-Nafisah

Master student at the Information Science Lab (ISL), in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student, Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.

Abdulrahman Aljedaibi

Electrical Engineering PhD student at CEMSE, King Abdullah University of Science and Technology (KAUST).

Abdullah Hamdi

Abdullah Hamdi is a Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career In 2016, Abdullah Hamdi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. After that, he joined KAUST to proceed in the same field of study and received his master degree in 2017 in Computer Vision track. Research Interest Machine Learning Robustness of Deep Learning Computer Vision

Abdullah Adil Albakry

Education and early career: I graduated with my bachelor’s degree in Electrical and Computer engineering with a mathematics minor from North Carolina State University (NCSU) in the United States. Now, I am a MS/PhD student at KAUST supervised by Dr. Ahmed Eltawil. Research Interest: Machine Learning and Communication Education Profile B.Sc., Electrical and Computer Engineering, North Carolina State University, United States, 2020.

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> artificial intelligence (165)

A blindfold approach improves machine learning privacy

1 min read ·
A query-based method for extracting knowledge from sensitive datasets without showing any underlying private data could resolve long-standing privacy concerns in machine learning.

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Towards Trustworthy News Recommendation Systems

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B3 L5 R5209

This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.

Design and Process Development of Graphene-Based Geometric Diodes for Enhanced Performance

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B2 L5 R5220

This thesis advances graphene geometric diodes (GGDs) by addressing fabrication, performance, and design challenges using innovative nanofabrication techniques and artificial intelligence-enhanced computational methodologies.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.

Professor Wolfgang Heidrich elected as a Fellow of the National Academy of Inventors and named a 2025 Optica Fellow

5 min read ·
KAUST Professor Wolfgang Heidrich has been named a Fellow of both the National Academy of Inventors (NAI) and Optica, recognizing his pioneering contributions to computational imaging, display systems and AI-driven optical technologies.
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Causality-based new drug development: Some successful cases and a new challenge

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B2 L5 R5209

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Paving the Way for Next Generation Wireless Data Center Networks

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B1 L4 R4214

As big data, articial intelligence, cloud services, cellular infrastructure, content delivery; all of which entail interconnected and sophisticated computing and storage resources. Recent studies on traditional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack trac, and unidentied ow multi-classes best known as delay sensitive mice ow (MF) and throughput-hungry elephant ow (EF).

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

Knowledge-based methods in biomedicine

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B9 L2 H1

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

KAUST-MBSC Invitational Healthcare Analytics and Data Science Workshop

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B3 L5 R5220

The amount of collected medical data and computational power are growing rapidly, which creates new opportunities to impact and improve healthcare by advanced analytics and data science. We aim to bring together practitioners and researchers from across the Kingdom to exchange knowledge and explore possible collaborations. To register, please, fill form here.

SIAM CSE21 (VIRTUAL) - Minisymposium on HPC Response to the COVID19 Pandemic

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KAUST

Abstract This minisymposium brings together experts in numerical simulation that have developed HPC software tools toward a better understanding coronavirus and the global COVID19 pandemic. Their recent results highlight the importance of computational science as a guiding tool that helps in uncovering the structure and mechanics of the viral spread as well as drastically reduces the number of candidate treatments that need laborious laboratory testing. From transformative medical advances to drug discovery driven by massively parallel supercomputing, this minisymposium will span a range of

The Robots are Coming – to your Farm

Girish Chowdhary, Associate Professor, UIUC

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KAUST

Featured Sessions
For more information please visit the website

Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

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B9 L2 H2

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

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B9 L2 R2322

Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.

Machine learning in hardware via trained metasurface encoders: theory, design and applications

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B2 L5 R5209

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

The Wild West of NLP Modeling, Evaluation, and Documentation

Dr. Nazneen Rajani, Research Lead, Hugging Face, California

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B2 L5 R5220

Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

The 2nd SAAI Factory Hackathon Kickoff Symposium at KAUST

The 2nd SAAI Factory Hackathon Kickoff Symposium 2023

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B20 Auditorium

We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaired by Christoph Faulhaber (Conceptual Artist, Film Maker) and Mohamed Elhoseiny (Computer Science Faculty at KAUST). About SAAI Factory Hackathon Established in 2021, SAAI Factory has been an initiative and one of the first contemporary art-based hackathons on Artificial Intelligence. The hackathon provides a framework for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to come together and explore the possibilities opened up by

Creating Cognitive Enterprises Through Decision Intelligence

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B9 L2 H2 H2

It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.

The Prospect of Generative Language Processing

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B9 L2 H2 H2

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

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B4 L5 R5220

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Data, AI and Cybersecurity: Synergies and Interconnection

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This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Thoughts About Machine Learning - Week 7

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The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Averaging, Momentum, and Schedulers in Optimization for Deep Learning

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In this talk, I will present some work in progress on practical optimization methods for deep learning. We will start with a discussion of several empirical techniques that enable training of large-scale models in language and vision tasks, including weight decay, averaging, and schedulers. We will then look at a new approach that we call schedule-free due to its ability to work without a pre-defined time horizon. I will share some details about the theory for these methods, explain why they might be useful in practice and then shed some light on their limitations. This talk will be oriented towards people who already have some knowledge of optimization methods.

KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine

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KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine will bring together clinicians, scientists and experts in data analysis and machine learning/artificial intelligence to showcase their research and modern developments in kidney paired donation.

Computer vision: Teaching computers how to see the world

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Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

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Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

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Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

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With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

The awesome scope of big data

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Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Sarah Alghamdi defends her master's thesis

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Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

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Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

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Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Azza Althagafi defends her Master's thesis

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Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

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Xiangliang Zhang, associate professor of computer science in the University's Computer, Electrical and Mathematical Science and Engineering division, recently gave an invited Early Career Spotlight Talk at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18) 2018. During the event—which was held in Stockholm, Sweden, from July 13 to 19—Zhang and 21 international peers presented on all areas of artificial intelligence (AI).

Tweaking tools to track tweets over time

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Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

KAUST Research Conference: AI in Medicine

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Student Focus: Adel Bibi

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KAUST Ph.D. student Adel Bibi, who works on bridging the gap between theory and practice in deep learning, believes we're living in a very exciting time.

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

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KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

AI learns complex gene-disease patterns

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A deep learning model improves the ability to identify genes potentially involved in disease.

Testing the blind spots in artificial intelligence

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Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

Database to support infectious disease research

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Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Symbol of change for AI development

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Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

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Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

AI reveals nature of RNA-protein interactions

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A deep learning tool could help in structure-based drug discovery.

ActivityNet is featured in the 2019 Artificial Intelligence Index

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ActivityNet is featured in the AI Index as the benchmark for the algorithms that can recognize human actions and activities from videos.

LD Motif Finder locates ancient hidden protein patterns

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A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

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The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

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Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

CEMSE Division startups in focus: Peregrine Genomics

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The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Flattening the sentimental curve

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-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

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A layer-based approach raises the efficiency of training artificial intelligence models.

KAUST CS student recognized with AAAI Outstanding Program Committee Award

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Konstantin Mishchenko, a Ph.D. student under the supervision of Professor Peter Richtárik, has been selected as one of the Association for the Advancement of Artificial Intelligence’s (AAAI) 12 Outstanding Program Committee Members for 2020. Mishchenko was presented with a certificate in recognition of his outstanding service at AAAI-20 held in New York from February 7-12.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

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Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

AI that's built to save lives

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Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

KAUST Prospective Student: Siyuan Chen

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Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Robert Hoehndorf promoted to associate professor at KAUST

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KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

How AI helps improve COVID-19 testing

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Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Former KAUST VSRP student wins Best Talk Award at ISMB 2020

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Runar Reve, a former KAUST Visiting Student Research Program (VSRP) student, recently received the International Society for Computational Biology’s (ISCB) Bio-Ontologies COSI – Best Talk Award at the 28th Conference on Intelligent Systems for Molecular Biology (ISMB) 2020.

Meet KAUST prospective student: Zhenwen Liang

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Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

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A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

Meet KAUST prospective student: Xiaopeng Xu

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Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.

Meet KAUST prospective student: Pratik Nag

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Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Meet KAUST student: Konstantin Burlachenko

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Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Feng Liang

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Feng Liang is a computer science and engineering graduate who will join KAUST from the Southern University of Science and Technology (SUSTech), China. Feng will join the University this fall as a M.S. candidate and member of the KAUST Nanovisualization Research Group under the supervision of Professor Ivan Viola.

Deep learning in the emergency department

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Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

KAUST startup tackles COVID using AI and robotics

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The COVID-19 pandemic has activated vital innovation and R&D initiatives to tackle the crippling effects, health ramifications, and overall impact of the coronavirus on global societies and economies. In recent months, the King Abdulaziz City for Science and Technology (KACST), based in Riyadh, Saudi Arabia, launched a COVID-19-themed virtual accelerator aimed at supporting talented innovators, aspiring entrepreneurs, and innovative small companies dedicated to finding solutions to mitigate the spread of the virus through protection, prevention, tracking/detection, and treatment solutions.

Broadening horizons for people with quadriplegia

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An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Brain-on-a-chip would need little training

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A neural network that mimics the biology of the brain can be loaded onto a microchip for faster and more efficient artificial intelligence.

Machine learning at speed

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Optimizing network communication accelerates training in large-scale machine-learning models.

Seven IVUL members recognized as Outstanding Reviewers by CVPR 2021

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The 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) has granted Outstanding Reviewer awards to seven members of IVUL: Alejandro Pardo, Ali Thabet, Chen Zhao, Mattia Soldan, Mengmeng Xu, Juan C. Pérez, and Silvio Giancola. CVPR, a premier annual computer vision event that will take place virtually from June 19th to June 25th, is the largest computer vision conference in the world, gathering researchers from academia and industry alike. The peer-review process through which the conference determines acceptance of papers relies on expert reviewers from across the world

SDAIA, KAUST sign MoU to develop AI research and innovation in Saudi Arabia

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Saudi Gazette report JEDDAH — The Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have signed a memorandum of understanding to unite efforts to develop Artificial Intelligence (AI) research and modern technologies in the Kingdom. The MoU was signed by the CEO of the National Center for Artificial Intelligence, the innovative arm of SDAIA, Dr. Majid Al-Tuwaijri, and by KAUST President, Professor Tony Chan, the Saudi Press Agency reported on Thursday. The MoU supports science and technology research and improves its quality

KAUST research group win inaugural MCIT Digital Innovation Award

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A team of KAUST researchers have won first place in the Digital Research track at the inaugural Digital Innovation Awards held on Sunday, April 25, 2021. Launched by the Ministry of Communications and Information Technology (MCIT), the awards aim to “enhance the culture of innovation and entrepreneurship in digital research and development” in Saudi Arabia.

Divided attention could ease wireless congestion

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Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Drop the zeros for a performance boost

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An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon

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A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.

Perovskites used to make efficient artificial retina

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KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Computer learns to write captions with feeling

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Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Steering Customized AI Architectures for HPC Scientific Workloads: A Bird of Feather at SC22!

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Abstract: We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems. We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid

BORG team win at the KAUST-NVIDIA GPU Hackathon 2022

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Congratulations to Senay Kafkas, Maxat Kulmanov and Fernando Zhapa Camacho of the Bio-Ontology Research Group (BORG) on winning at the KAUST-NVIDIA GPU Hackathon 2022. The 8th edition of the Hackathon on Accelerating Scientific Applications using Graphical Processing Units (GPUs) was held at King Abdullah University of Science and Technology (KAUST), twelve teams of students and researchers enjoyed the opportunity to accelerate their codes in GPU’s under the guidance of expert mentors of NVIDIA, Codee, KAUST Supercomputing Lab (KSL) and KAUST core labs.

MS Thesis Defense | Learning 3D structures for protein function prediction

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Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

PhD Dissertation Defense | Ontology design patterns and methods for integrating phenotype ontologies

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CBRC PhD student Sarah Alghamdi successfully defended her dissertation last week. She has been an essential member of the Bio-Ontology Research Group (BORG) since 2015.Her research interests include the applications of artificial intelligence and statistical methods to genomics and healthcare.

PhD Dissertation Defense | Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

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CBRC PhD Student Azza Althagafi successfully defends her dissertation under the supervision of Associate Professor Robert Hoehndorf. She has been a member of the Bio-Ontology Research Group (BORG) since 2016. Her research interest lies in the crossroads of computer science and biology, leveraging AI techniques to explore the human genome's intricacies and its link to various diseases.

A new approach to rare disease diagnosis

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An innovative AI-based tool leverages varied symptom descriptions, along with evidence from the scientific literature and genomic datasets, to pinpoint disease-associated gene variants.

Safeguarding the right to be forgotten

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An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

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Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

AI tool predicts function of unknown proteins

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Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.

Former CBRC PhD Student Ibrahim Alabdulmohsin Highlighted for AI Contributions on Saudi National TV

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Former CBRC PhD student Dr. Ibrahim Alabdulmohsin, now a senior research scientist at Google DeepMind in Zurich, was featured by the Josoor Program on Saudi National TV. The program, meaning "Bridges" in Arabic, highlighted Dr. Alabdulmohsin's journey from his education at the University of Nebraska, Lincoln, and Stanford University to his leadership roles at Aramco's Advanced Analytics team. After graduating from KAUST under the supervision of Prof. Xin Gao, he joined DeepMind in 2019, contributing to groundbreaking AI research. The 24-minute episode explores his career and contributions to the field. Dr. Alabdulmohsin's commitment to advancing AI underscores his pivotal role in shaping the future of technology.

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Ángel López Oriona

Postdoctoral Research Fellow, Statistics
Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Victor Escorcia

Research Scientist committed to innovating AI agents to empower creativity and increase productivity. Passionate about developing cutting-edge AI applications involving video understanding and creative storytelling. I have a wide range of research and engineering interests from on-device AI to General AI. Reach out!

Tagreed Khalil

Tagreed Khalil joined Kaust in 2011, Initially, she have joined the Government Affairs department in November 2013 as an Administrative Assistant, thereafter, in January 2016, she continued with the Administrative Assistant role in the CEMSE division. Currently, she is working as a Business Administrator in the Artificial Intelligence (AI) Initiative, at King Abdullah University of Science and Technology (KAUST).

Silvio Giancola

Silvio Giancola is a Research Scientist at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL), part of the Center of Excellence for Generative AI (GenAI).

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling

Safa Khemiri

Safa Khemiri is an MS/PhD candidate at the Communication Theory Lab (CTL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career In 2018 and after being ranked among the top 2% at the Tunisian national contest for entrance to engineering schools, Safa Khemiri joined the best engineering school in Tunisia, "Ecole Polytechnique de Tunisie (EPT)" where she received multidisciplinary training. In her final year, she specialized in signals and systems. Before joining KAUST for her Master’s degree, Safa had

Runar Reve

Visiting Student, Bio-Ontology Research Group
​Runar Reve is from Klepp, Rogaland, in Norway. He is currently in his industry year as a visiting student for his bachelor's degree in Artificial Intelligence and Robotics at Aberystwyth University in Wales and will obtain his degree by the summer of 2021. He is currently working with Professor Robert Hoehndorf Bio-Ontology Research Group (BORG) on genome-wide association study (GWAS) to find variants, or sets of variants, in genomes that can result in genetic disease.​ Research Interests Genome-Wide Association Studies​. Education ​Undergrad in Artificial Intelligence (with integrated year

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Nojood Almayouf

Nojood Almayouf is a Graduate Research Assistant in the Distributed Systems and Autonomy (DSA) Lab within the Robotics, Intelligent Systems and Control (RISC) Research Initiative. Education M.S. in Electrical Engineering, KAUST, Saudi Arabia (Aug 2017 - Dec 2019). Visiting graduate student, Stanford University, United States (Jun 2017 - Aug 2017). B.S. (with first honor) in Electrical and Computer Engineering, Effat University, Saudi Arabia (Jan 2013 - Jun 2017). Professional Profile Graduate Teaching Assistant for ECE 271A/ME 221A: Linear Control Systems Course, KAUST (Aug 2022 - Dec 2022)

Mohammed Abdullah Hussaini

Senior Embedded System Engineer at KAUST (King Abdullah University of Science and Technology)
KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Maria Sara Nour SADOUN

Visiting Student (former), Information Science Lab
Research internship in King Abdullah University of Science and Technology within EMANG as a Visiting Student on the topic of Epileptic Seizure Detection using Electroencephalography (EEG).

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Manal A. Alshehri

Ph.D. Student, Computer Science

Klemens Katterbauer

Klemens Katterbauer is a AI specialist and software engineer working on robotics and artificial intelligence software development at EXPECARC in Saudi Aramco. His focus is on bridging the gap between sustainable energy transition development and the power of artificial intelligence. Klemens has published more than 50+ journal and conference publications, and has more than 20+ granted and submitted patents. Before this he was the IT Infrastructure Manager and Business Process Consultant for Eastern Europe, Middle East & Asia Pacific Region at the XXXLGroup (2nd largest furniture retailer in the

Khalil Allouch

Khalil Allouch is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Khalil Allouch has studied mathematics and physics at the University of Tunis and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2019. In 2022, he received his Engineering Diploma with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. Early Career Before joining the MS/Ph.D. program at KAUST, he had research experience with KAUST as a visiting

Kexin Niu

Ph.D. Student, Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Kerven Durdymyradov

Ph.D. Student, Computer Science
Honors and Awards Bronze Medal and Honourable Mention- International Mathematical Olympiad (IMO). Netherlands–Kazakhstan (2011, 2010) 1st and 2nd place Winner - Open Doors International Olympiad - Mathematics & AI track. Russia (2022, 2020) Silver and Bronze Medals - Balkan Mathematical Olympiad (BMO). Romania-Moldova (2011, 2010) Silver and Bronze Medals - South Eastern Mathematical Olympiad (SEEMOUS). Romania-Greece (2014, 2013)

Jürgen Schmidhuber

Professor, Computer Science
Professor Schmidhuber is a renowned computer scientist, best known for his pioneering work in artificial intelligence (AI), deep learning and artificial neural networks. He has authored more than 350 peer-reviewed papers and has provided AI strategies to several governments.

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Haozhe Liu

Ph.D. Student, Computer Science
Haozhe Liu is a PhD student at AI Initiative, King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Jürgen Schmidhuber.

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.

Frankly Toro

Frankly Toro is a Ms/Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career He is a graduate of National University of Colombia where he received a B.S. degree in Mechatronics. Education Profile B.S., Mechatronics , National University of Colombia, 2020

Ferdaous Tarhouni

Ferdaous Tarhouni is an MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Ferdaous has studied mathematics and physics at the Preparatory Institute for Engineering Studies of Tunis and has been ranked among the top 2% in the national engineering school exam in 2020. This enabled her to enter the Ecole Polytechnique de Tunisie (EPT), one of the most prestigious engineering schools in Tunisia where she received multidisciplinary training. In her final year, she specialized in

Fellipe Augusto Araújo Leandro

Fellipe received his bachelor degree in Electrical Engineering from Federal University of Rio Grande do Norte (UFRN), Brazil, in 2019. During his undergraduation, he worked in researches related to microcontroller-based embedded systems platforms, with focus in embedded software programming and PCB design. In 2019, he worked as an intern in a startup company developing embedded software for a Lora-based biotelemetry system, and also in another startup developing PCB design for low-cost Educational robotics and electronics. In 2020, he joined KAUST to research under the supervision of Professor

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Fares Fourati has studied mathematics and physics at the University of Sfax and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2017. In 2020, he received his diplôme d’ingénieur with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. In 2022, he received his Master of Science in electrical and computer engineering from KAUST. Early Career Before

Feng Liang

Feng Liang is a former member of the Nanovisualization Research Group at KAUST lead by Professor Ivan Viola. Before joining KAUST, Feng obtained a bachelor's degree in computer science and engineering from the Southern University of Science and Technology (SUSTech), China. Research Interests Feng's research interests include artificial intelligence and computer graphics.

Fat-hy Omar Rajab

Master's Thesis Abstract The multi-agent robotic system has been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased efficiency in multi-agent systems is achieved by the execution of the task in a cooperative manner. But to achieve cooperation in multi-agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent systems rely on the use of the GPS to provide global positioning information which suffers great deterioration in performance in indoor applications, and also

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as

Chi-Han Peng

Research Scientist, Computer Science
Chi-Han Peng was a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). He is an Assistant Professor at the School of Information Science and Technology (SIST) at ShanghaiTech University. Education and Early Career Before joining KAUST, Dr. Peng was a postdoctoral research associate at University College London (UCL), working with Prof. Niloy Mitra. He worked at Nogle, a private company based in Taipei, on the 3D modeling and simulation of real-world clothes for the fashion/garment industries

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Amer AlGhadhban

Amer AlGhadhban is a Ph.D. student under the supervision of Professor Basem Shihada at Networking Research Lab in King Abdullah University of Science and Technology (KAUST). Amer obtained both of his degrees B.S. and M.Sc. in Computer Engineering from King Fahd University of Petroleum and Minerals (KFUPM). He was a Cisco Academy instructor and during that he earned multiple professional certificates: Cisco Certified Network Associate (CCNA), Cisco wireless, SANS-GIAC Certified Firewall Analyst (GCFW-Gold), Certified Ethical Hacker (CEH), Security Certified Network Engineer (SCNP), and SANS

Alexandra Aguiar Gomes

Alexandra Aguiar Gomes has been a university educator since 1996. Her area of expertise is optimization, with current interests in the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. Alexandra finds joy in simplifying mathematical concepts, helping her students discover the practicality of mathematics in their fields.

Alejandro Pardo

Alejandro Pardo is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) program under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Alejandro has an Engineer’s degree in Artificial Intelligence in both Electronics and Biomedical Engineering from the University of Los Andes in Colombia in 2017. He continued his studies at the University of Los Andes and received his Master’s Degree in Biomedical Engineering in 2018. He visited KAUST as a

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Ahmed Ishak Patwa

Visiting student at King Abdullah University of Science and Technology, Systems Engineer at Qawam for Research and Studies, Jeddah, Saudi Arabia.

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Abdurauf Abdurakhmanov

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)
<> statistics (124)

Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

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B1

In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.

Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

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B1 L4 R4102

We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Multivariate Functional Data Analysis and Visualization

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B3 L5 R5220

As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Finding the Face in the Crowd

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An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

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Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

2015 Workshop on Computational Space-Time Statistics

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Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

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The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Postdoc Luigi Lombardo gets an Outstanding Student Poster and PICO EGU Award. Congratulations Luigi!

1 min read ·
The 2016 Outstanding Student Poster and PICO (OSPP) Award is awarded to Luigi Lombardo for the poster/PICO entitled Landslide triggering-thickness susceptibility, a simple proxy for landslide hazard? A test in the Mili catchment (North-Eastern Sicily, Italy). Regarding his work, Luigi said: " In the PICO presentation project we modeled the thickness at the crown of each landslide as the dependent variable. As a result, the predictive maps carried more information than classic susceptibility ones, enabling a better proxy to hazard prediction." You can find Luigi's poster here.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

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A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

2016 Workshop Statistics for High-Dimensional & Complex Data

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Workshop on Statistics for High-Dimensional and Complex Data, Nov. 6-9, 2016​ Organizers : Prof. Marc G. Genton, Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. David Bolin, Chalmers University of Technology, Sweden Prof. Probal Chaudhuri, Indian Statistical Institute, India Prof. Dan Cooley, Colorado State University, USA​ Prof. Subhajit Dutta, IIT Kanpur, India Prof. Amanda Hering, Baylor University, USA Dr. Anna Kiriliouk, Université

Getting the jump on industrial failures

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A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Smart healthcare uses stats to spot a stumble

1 min read ·
Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Going to extremes to predict natural disasters

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A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Professor Sun receives the Environment Early Investigator Award 2017

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Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Assistant Professor Ying Sun wins American Statistical Association award

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​​​​Ying Sun, KAUST assistant professor of applied ​mathematics and computational science​, won the 2017 Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA). Sun received her award at the 2017 Joint Statistical Meetings (JSM), held from July 29 to August 3 in Baltimore, Maryland, U.S. Sun's award recognizes her important contributions to the field of environmental statistics that help to address major problems like climate change and environmental pollution. At KAUST, Sun leads the Environmental Statistics Group in the

Dataset size counts for better predictions

1 min read ·
A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

2017 Workshop Modern Statistics for Complex Data Structures

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2017 Workshop Modern Statistics for Complex Data Structures

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

One bad apple can spoil a robot swarm

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Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Modeling where the wind blows

1 min read ·
A new statistical technique could be used to more accurately locate global wind resources.

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

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Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

1 min read ·
Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

PhD student Sabrina Vettori selected for an ENAR Distinguished Student Paper Award. Congrats!

1 min read ·
PhD student Sabrina Vettori has been selected by the International Biometric Society Eastern North American Region (ENAR) to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting in Atlanta ( https://www.enar.org/meetings/spring2018/index.cfm ) for her manuscript entitled " Bayesian Hierarchical Modelling of Air Pollution Extremes Using Multivariate Max-Stable Processes ". She will present her work at an oral contributed paper session, be invited to attend the ENAR President’s reception and publicly receive their award at the beginning of the ENAR President’s Invited

Revealing hidden relationships in data

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A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

1 min read ·
Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Pulling rank on spatial statistics

1 min read ·
A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Challenging statistics of weather extremes

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More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Zooming in on brain function

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Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

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KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Second year Ph.D. student to receive top statistics award

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KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Ph.D. Student Yuxiao Li won the ENVR student paper award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Connecting the dots to unlock brain structure

1 min read ·
Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

How extreme weather exacerbates air pollution

1 min read ·
Simultaneously modeling of air pollutants and weather under extreme conditions highlights the potential for serious health risks.

Searching for wind for the future

1 min read ·
The first quantification of wind energy in Saudi Arabia points to high wind power potential for many decades to come.

Reining in computational complexity

1 min read ·
A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

Solving real-world problems

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A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

2018 Workshop on Statistics and Data Science

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The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

Stimulating deeper insights into brain function

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Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

New stats apps show a virtual reality

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Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

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Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Students Peng Zhong and Rustam Bekishev get their MS degree

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On December 14, 2018, KAUST welcomed 243 graduates at the University’s ninth Commencement ceremony. Among them, students Rustam Bekishev and Peng Zhong, who is now a Ph.D. student In Statistics in our group, got their MS degree. Congratulations!

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

1 min read ·
Carolina Euán, a postdoctoral fellow in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) at the 28th Annual TIES Conference 2018 held from July 16 to 21, 2018, in Guanajuato, Mexico. Euán won the award for her talk entitled "Bernoulli Vector Autoregressive Model with Applications to Spatio-temporal Drought Events in Mexico."

Clustering helps unlock secrets of the human brain

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The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Simple statistics can be good enough

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Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Taking aim at outliers

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Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Cutting datasets down to size

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A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Epilepsy study shows the shape of things to come

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Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

3 Faculty Positions in Statistics 2020 at KAUST

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The 3 faculty positions are in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in one of the following areas: (1) Statistical Data Science and AI, including network data analysis and high-dimensional statistics (https://apply.interfolio.com/69165); (2) Statistical Climatology, with expertise in statistical analysis of climate model output data, in particular regional climate models, and in physical systems (https://apply.interfolio.com/69167); (3) Statistics for Public Health, including smart health data analysis, personalized medicine, and disease mapping (https://apply.interfolio.com/69168).

Marc Genton received an ISI Service Award 2019

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Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Shaking up the landslide model

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Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

2019 Statistics and Data Science Workshop

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From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

Testing barley's salt tolerance is a numbers game

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Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

A second wind for turbine design

1 min read ·
Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.

Pop stats for big geodata

1 min read ·
A universal high-performance computing interface allows popular statistical tools to run efficiently on large geospatial datasets.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

1 min read ·
The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Today we celebrate World Statistics Day!

1 min read ·
Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Modeling the neighborhood boosts landslide prediction

1 min read ·
A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Mixing precision for model acceleration

1 min read ·
A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Heatwave model shows it is hotter in more places, more often

1 min read ·
New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Getting to the bottom of deadly weather

1 min read ·
Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

1 min read ·
Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Xiran Zhang

Ph.D. Student, Statistics

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Sihan Chen

Ph.D. Student, Statistics

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Sagnik Mondal

Ph.D. Student, Statistics
Sagnik Mondal is a PhD student in the Statistics program at KAUST. Prior to joining KAUST, Sagnik completed a Master of Science degree from the Indian Institute of Technology (IIT), Kanpur, India.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.

Mary Lai O. Salvana

Ph.D. Student, Statistics
Mary Salvaña is a PhD student in the Spatio-Temporal Statistics and Data Science group under the supervision of Prof. Marc Genton at the King Abdullah University of Science and Technology (KAUST). Education and Early Career MS Applied Mathematics graduate from the Ateneo de Manila University (Philippines) Research Interest Spatio-Temporal Statistics, Wind Energy Modelling, Renewable Energy Modelling.

Kesen Wang

Ph.D. Student, Statistics
Kesen Want is a MS student in the Statistics program at KAUST. Prior to joining KAUST, Kesen completed a Bachelor of Science degree from the University of Maryland College Park in the USA.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Hanan Alahmadi

Ph.D. Student, Statistics
Hanan Alahmadi is a PhD candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Education Profile M.Sc. Statistics, KAUST, 2021 B.Sc. Mathematics, Taibah University , 2017 Research Interest Geospatial data analysis and health surveillance Spatio-temporal disease data Statistics and spatial epidemiology Geospatial modeling Disease mapping Location Building 1, Al-Khawarizmi, 4th floor, 4212-WS12 Publication Joint Quantile Disease Mapping

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Ercong Zhang

M.S. Student, Statistics
Ercong Zhang is a MS/Ph.D. student in the Statistics program at KAUST. Prior to joining KAUST, Ercong obtained his Bachelor of Science degree in Statistics from Southern University of Science and Technology (SUSTech), Shenzhen, China.

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim
<> Deep learning (110)

FMCW Radar Applications for Automotive and Biomedical Applications

-

B1 L3 R3119

This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.

Israel Jesus Santos Filho

MS Student, Electrical and Computer Engineering ,work under the supervision of Professor Tareq Al-Naffouri at King Abdullah University of Science and Technology.

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Anum Ali

Intern (former), Information Science Lab
Research Engineer KAUST (King Abdullah University of Science and Technology).Senior engineer at Samsung Research America.
<> sensors (99)

FMCW Radar Applications for Automotive and Biomedical Applications

-

B1 L3 R3119

This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.
Olga Krestinskaya

International recognition for KAUST Ph.D. candidate Olga Krestinskaya

3 min read ·
Olga Krestinskaya, a KAUST Ph.D. student, wins Best Poster at the 2024 Nature Neuromorphic Computing Conference for her work on energy-efficient AI hardware.

Implantable and Wearable Microelectronic Devices to Improve Quality of Life for People with Disabilities

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B3 L5 R5209

Implantable medical devices (IMD) and neuroprostheses are finding applications in new therapies thanks to advancements in microelectronics, sensors, RF communications, and medicine, which have resulted in embedding more functions in IMDs that occupy smaller spaces down to millimeters and consume less power, while offering therapies for more complex diseases and disabilities. I will address the latest developments in key building blocks for state-of-the-art IMDs.

Design and Optimisation of Next generation Compact Photonic Sensors

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B3 L5 R5209

Although optical sensors incorporating grating inscribed and etched fibres are now sufficiently mature and well established in the market, however, designs based on more exotic nanowires and photonic crystal fibres are becoming increasingly important and showing much improved sensitivity by accessing a larger evanescent field. Similarly, novel planar design concepts, such as the silicon slot guide-based design is showing even greater promise, allowing the exploitation of well-developed CMOS fabrication technologies for potentially low-cost sensor elements.

Flexible, Wearable, Disposable Wireless Communication and Sensing Systems Through Additive Manufacturing - 2019

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B9 L2 H1

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems.

Integration Strategy for Standalone Compliant Interactive Systems for Add-on Electronics

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B9 L3 R3125

Physically compliant electronics are scientifically intriguing, mechanically complex, ‎technologically ‎challenging but with huge socio-economical potential. Until now the target applications ‎for ‎flexible electronics have been limited to displays, solar cells, printed batteries, wearables, and ‎implantable. However, with the emergence and growth of Internet of Things (IoT) devices ‎worldwide ‎from nearly 27 billion in 2017 to 125 billion in 2030, in this PhD research, expanding the ‎horizon of ‎applications for flexible electronics toward existing “things” will be explored. Low-cost sustainable materials as active electronic materials and a ‎Do-It-Yourself (DIY) integration strategy is used to build “Add-on” standalone sensory system which can be ‎attached to any ‎existing things like a decal. Such electronics can also be embedded in newly minted ‎devices specially ‎using additive methods.

Liquid Metal-Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

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B2 B3 A0215

In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon counterparts and extend their applications.

Prof. Atif Shamim Elevated to Senior Member of IEEE

1 min read ·
Prof. Atif Shamim has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

Dr. Atif Shamim appointed Associate Editor for IEEE Transactions on Antennas & propagation

1 min read ·
Dr. Atif Shamim has been appointed Associate Editor for IEEE Transactions on Antennas & Propagation (TAP). He has been included in the editorial board due to his work on "Co-design of on-chip antennas and circuits on CMOS platform".

Prof. Khaled Salama appointed Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs

1 min read ·
Prof. Khaled N. Salama was appointed Associated Editor for IEEE Transactions on circuits and systems-II: Express Briefs (TCAS II) for the period 2014-2015.

Prof. Khaled Salama's Work On Fractional Order Element Based Impedance Matching, Granted US Patent US 8762114

1 min read ·
Prof. Khaled Salama in collaboration with Dr. Ahmed Radwan and Prof. Atif Shamim obtained a US Patent

Muhammad Fahad Farooqui and Ahmed Nafe's paper selected as best paper

1 min read ·
Paper by KAUST students Muhammad Fahad Farooqui and Ahmed Nafe from Prof. Atif Shamim's group has been selected as the best paper in the "Inkjet-Printed Components category".

Professor Jurgen Kosel Elevated To Senior Member Of IEEE

1 min read ·
Prof. Jurgen Kosel has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

A novel design of antenna array developed by KAUST scientists

1 min read ·
At the sensors lab, Prof. K.N. Salama and Prof. A. Shamim led a team of KAUST founding masters students Farhan Abdul Ghaffar and Muhammad Umair Khalid to develop an aperture coupled patch antenna array SoP.

PhD Students Invited to 65th Lindau Nobel Laureate Meeting

1 min read ·
Two CEMSE doctoral students have been invited to the 65th Lindau Nobel Laureate Meeting, which will be held on June 28-July 3.

An origami 'slinky' that harvests incidental energy

1 min read ·
Lightweight and low-cost device uses friction inside paper-based coils to transform mechanical motion into electricity.

KAUST Researchers Offer 'Touch' To The World

1 min read ·
We proudly announced that he had won the award for the "Best Research Paper" at the 2015 IEEE Sensors Applications Symposium for his research on artificial skin.

CEMSE Bridging science: IEEE-NEMS best paper award bestowed to CEMSE for scientific boundaries crossing

1 min read ·
Scientific boundary-crossing initiatives are what help scientists stepping across boundary lines that traditionally mark the divide between scientific disciplines.

This is your light speaking, how can I help you?

1 min read ·
Prof. Salama's Smart Lighting System won the 1st prize at WEP Science Fair with a device that will change the way we interact with light.

Making Artificial Skin inspired from nature

1 min read ·
Congratulations to Professor Jurgen Kosel for his research on an Artificial Skin inspired by nature. Research that was featured in MIT Technology Review.

Better routing for wireless networks

1 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

New low cost diagnostic for cardiovascular risk detection

1 min read ·
A new flexible, low-cost cardiovascular risk marker biosensor was developed by Prof. Khaled Salama and his team at KAUST University, Saudi Arabia.

Miniature flexible sensor to detect heart disease

1 min read ·
A new biosensor made of laser-etched electrodes on a gold-coated polymer may provide an effective and cost-efficient way to assess heart disease risk.

PhD student Ahmed Alfadhel interviewed in New Scientist weekly

1 min read ·
In an interview with New Scientist weekly, PhD student Ahmed Alfadhel from Prof. Jurgen Kosel's team spoke about his research on bio-inspired artificial skin.

Published paper: Single readout technique published at Nature Scientific Reports

1 min read ·
Prof. Khaled Salama and his team managed to achieve the theoretical limit of a single memory access per pixel for a gateless memristor array readout at a fraction of the power of state-of-the-art readout techniques.

Jurgen Kosel delivers seminar at Berkeley Sensor and Actuator Center

1 min read ·
The Berkeley Sensor & Actuator Center (BSAC), founded in 1986 as the National Science Foundation Industry/University Cooperative Research Center for Microsensors and Microactuators. Professor Kosel spoke about magnetic micro- and nanosystems research being conducted at the Sensing, Magnetism, and Microsystems group at KAUST.

Professor Khaled Salama awarded US patent on MEMS fractal capacitors

1 min read ·
Patent application US 20140239446 A1 on "Fractal Structures For Fixed MEMS Capacitors" was awarded a USA patent.

Three new patents for Professor Khaled Salama and the Sensors Lab Team

1 min read ·
With the latest project on Digital Chaos Systems, KAUST's Associate Professor of Electrical Engineering Khaled Salama and the Sensors-Lab's team (http://sensors.kaust.edu.sa/research/current/digital-chaos-systems) secured stunning achievements and three key patents, opening new frontiers for cybersecurity, cryptography, and electronic devices.

Prof. Salama's paper hits the top for 6 months in the row!

1 min read ·
In August 2016, Prof. Salama's article "Stochasticity Modeling in Memristors", was for the sixth time consecutively among the most downloaded paper of the month and the most read article in the IEEE Transactions on Nanotechnology (TNANO).

Flash Microfluidics, rapid Lab on a Chip fabrication method for cost- effective in situ production.

1 min read ·
From DNA sequencing to environment monitoring, microfluidics devices have been broadly employed, but their production is more and more time-consuming and expensive. Researchers from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, have developed an innovative prototyping method for Lab on a Chip (LOC) production to plummet costs while augmenting performances.

Ph.D. student Muhammad Akram Karimimi among Best Paper Award finalists

1 min read ·
Ph.D. student Muhammad Akram Karimimi from the research team of Atif Shamim, Assistant Professor of Electrical Engineering at KAUST and Director of IMPACT Laboratory, was one of the finalists in the Student Paper Competition at the International Microwave Symposium held in San Francisco, California, in May 2016.

Professor Shamim's Smart Bandage hits Nature Scientific Report and win the IEEE MECAP' 16 Best Paper Award

1 min read ·
Professor Shamim's new cutting edge, flexible and low-cost technology to monitor chronic wounds recently put CEMSE on the map of health care system research. His scientific paper " Low-Cost Inkjet Printed Smart Bandage for Wireless Monitoring of Chronic Wounds " appeared on Nature Scientific Reports and won IEEE MECAP'16 - Middle East Conference on Antennas and Propagation - Best Paper Award, in Beirut, last September.

Gentle sensors for diagnosing brain disorders

1 min read ·
New sensor design paves the way for safer and more effective brain monitoring.

Keeping pollution under control

1 min read ·
Sophisticated statistical methods can detect ozone pollution hot spots and monitor instrument failure.

KAUST-born robotic avatar sets new era for undersea archaeology

1 min read ·
In 2015, an extensive archaeological programme led by the French Ministry of Culture and the University of Montpellier set up a scientific collaboration with Khaled Nabil Salama, Professor from CEMSE Division and co-worker Professor Christian R. Voolstra, from the KAUST Red Sea Research Center, in partnership with Stanford University and Meka Robotics, in California, to develop Ocean One, an agile avatar that affords immediate and haptic-visual interaction – the process of recognizing objects through touch - in the ocean environment at depths greater than 50m.

Rolling out an e-sticker revolution

1 min read ·
High-speed fabrication developed at KAUST can turn out adhesive and flexible electronic devices in any shape imaginable.

Wrist sensor for health screening

1 min read ·
Give a Post-It Note paper to an Electrical Engineering team and you will see it turn onto the new alley to revolutionize modern medicine; an unprecedented wearable, budget-friendly and reliable a paper-based sensor for health screening encapsulated in a silicon wrist with a recycling high-tech design. Body temperature, sweat levels, heart rate and blood pressure can now be monitored with no need to go to hospitals nor using costly technologies, dramatically rare in underdeveloped or developing countries.

Feeling the heat in cells

1 min read ·
New nano-sized sensors provide unprecedented data on how heat diffuses in and out of living cells.

The next generation of sensing platforms

1 min read ·
The University welcomed 70 delegates to the third annual KAUST Sensor Initiative, held on campus from May 7 to 8. The interdisciplinary meeting aimed to transform sensor technologies and explore novel applications, create a global group of sensor pioneers and mentor a new generation of sensor experts.

Akram presented a poster and gave a live demo in IEEE Sensors Conference

1 min read ·
Muhammad Akram Karimi, Ph.D. student in IMPACT Lab, presented a poster titled "A wearable 3D motion-sensing system integrated with a Bluetooth smartphone application-A system-level overview" at IEEE Sensors conference held in Glasgow, Scotland on Nov 1, 2017.

UESTC students visit CEMSE Laboratories during a one week Winter Camp at KAUST

1 min read ·
Twenty-two undergraduate students from the University of Electronic Science and Technology of China (UESTC), Chengdu, are currently visiting the CEMSE Division as part of one week Winter Camp at KAUST.

The raw power of human motion

1 min read ·
Standalone power modules that harvest and convert vibrations from their surroundings into electricity could soon fuel future microsystems.

Lining MOF pockets to detect noxious gases

1 min read ·
A porous material with tailor-made pockets stitched into its structure is a promising material for sensing noxious gases. A thin film of the material, coated onto an electrode, formed an electronic sensor that could detect traces of sulfur dioxide gas. The sensor is a significant step toward real-world devices that can sniff out dangerous gases in real air. Although several lab-based analytical instruments can detect traces of a specific gas in the air, these instruments are typically large, expensive, power-hungry machines. There is still a need for small, inexpensive, energy-efficient

Somayah Albaradei wins at City of the Future competition

1 min read ·
​CBRC Ph.D. student Somayah Albaradei won first place in the People's Choice category of the 2018 Spring Enrichment Program's ePoster-City of the Future competition. She won an iPad Pro and received a certificate of recognition.

Hussain is taking a flexible approach

1 min read ·
“The theme of our research is how we can empower humanity with technology,” said Muhammad Mustafa Hussain, professor of electrical engineering at KAUST. Using the skills from his time in industry, and the support he is granted by KAUST, Hussain creates flexible, wireless electronics to inexpensively turn everyday objects into smart devices. When Hussain had his novel computer chip technology licensed by Intel Corporation before he had even graduated from his Ph.D., he soon found himself a rewarding job in the semiconductor chip industry. But Hussain harbored the desire to one day go into

The internet of sea things

1 min read ·
When Atif Shamim, KAUST associate professor of electrical engineering, was approached by colleagues in the University's Red Sea Research Center (RSRC) with the challenge of developing underwater sensing technology for marine animals, he wasn't sure how his expertise could help, since marine animals are under water most of the time. Up until now, his research has focused on Bluetooth for applications on dry land, such as wearable medical devices connected to smartphone applications through Bluetooth communication protocol

KAUST partners with McLaren Racing on R&D

1 min read ·
McLaren Racing and KAUST signed a five-year research and development (R&D) agreement focused on extreme performance technology. The partnership aims to advance research in the areas of computational fluid dynamics (CFD), machine learning, fuels and lubricants, advanced mathematics and sensors and electronics. "This partnership underlines our commitment to supporting global STEM advancements," said Jonathan Neale, chief operations officer of McLaren Group. "KAUST is a leading research and education institution in these fields and mirrors our core values. McLaren Racing is part of a world

Paper sensors remove the sting of diabetic testing

1 min read ·
Inkjet-printed device helps monitor a patient’s blood sugar levels without painful needles.

3D-printed cube dials into energy harvesting

1 min read ·
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.

Marine sensor gets to grips with salt

1 min read ·
Laser-induced graphene formation creates tailor-made sensors for monitoring ocean creatures and ecosystems.

Student Focus: Muhammad Akram Karimi

1 min read ·
Muhammad Akram Karimi describes himself as an inquisitive-minded researcher who loves to solve industrial challenges through science and technology. Karimi is a third year Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab under the supervision of Professor Atif Shamim. His current research focus is on the design of low-cost and printed microwave sensors for various industrial applications, with a prime focus on the oil industry.

MOFs can sense and sort troublesome gases

1 min read ·
Fluorinated metal-organic frameworks make excellent materials for selective sensing and removal of toxic gases.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Putting the sense in materials

1 min read ·
An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.

Smart pill bottle keeps drugs safe

1 min read ·
Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.

KAUST Ph.D. student wins Three Minute Thesis competition

1 min read ·
Muhammad Akram Karimi, a fourth-year KAUST Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab headed by Associate Professor Atif Shamim, won the Three Minute Thesis (3MT) competition held during the IEEE Microwave Theory and Techniques Society's International Microwave Symposium (IMS2019) in Boston, Massachusetts, U.S, in early June. IEEE IMS has been taking place for 60 years, and it is the flagship conference for microwave engineers and scientists.

Democratizing electronics one step at a time

1 min read ·
"You would not believe how many amazingly talented people there are in the world, but they often are just not exposed to opportunities," noted Muhammad Mustafa Hussain, KAUST professor of electrical engineering and currently a visiting professor at the University of California, Berkeley. He is determined to try and change this—one step at a time.

Single-electrode material streamlines functions into a tiny chip

1 min read ·
Ruthenium oxide is used to integrate energy-storing microsupercapacitors and thin-film electronics at the transistor level.

Relishing the ‘McLaren experience’ at the 2019 Formula 1 Bahrain Grand Prix

1 min read ·
Altynay Kaidarova is a Ph.D. student in electrical engineering based in the University's Computer, Electrical and Mathematical Science & Engineering division.

Professor Kosel Appointed as Distinguished Lecturer of the IEEE Sensors Council

1 min read ·
Jürgen Kosel, associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, has been appointed as a Distinguished Lecturer of the IEEE Sensors Council for the period of 2020-2022.

Magnetic skin ensures the force is with you

1 min read ·
A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.

Sensor cube helps keep fish farming afloat

1 min read ·
A self-powered water quality sensor could help fish farmers to monitor pollution in their ponds remotely.

Saudi researcher’s magnetic skin research garners widespread attention

1 min read ·
Imagine if there was a revolutionary wearable technology that could enable the user to open doors or operate machinery with a simple wave of their hand or a mere blink of their eye? What people might not realize is that this contact-free human-machine technology already exists—and it has been developed right here in a laboratory at KAUST.

Sensing a heart attack before it strikes

1 min read ·
Nanomaterial-based electronic device monitors a key heart health biomarker.

MMH Labs named 2020 Edison Awards Gold Winner

1 min read ·
The KAUST MMH Labs were recently named a 2020 Edison Awards Gold Winner in the Consumer Electronics and Information Technology category for their wearable technology tagging prototype, Bluefin.

Exciting New Technology Makes BPA Water Testing More Efficient

1 min read ·
In today’s world, it should come as no surprise that plastic dominates the products that we rely on each and every day. From our technology devices, to our water bottles, plastic is almost always an integral structural component.

Light sensors detect larval pests munching on date palms

1 min read ·
Optical fibers wrapped around date palm trunks could help detect this tree’s most destructive pest early enough to save it.

Early warning sensor sniffs out cities' harmful gas

1 min read ·
An integrated detector device could form the basis of a distributed air-quality sensor network.

A fresh sense of possibility

1 min read ·
A multisensory graphene-based skin can sense in extreme environments where other sensors cannot be used.

Sensors get a laser shape up

1 min read ·
Laser writing breathes life into high-performance sensing platforms.

KAUST alumni receive SPE 2020 Regional Awards

1 min read ·
KAUST alumni Dr. Chinthaka Gooneratne and Dr. Bodong Li both recently received Society of Petroleum Engineers (SPE) 2020 Regional Awards for the Middle East and North Africa Region. Gooneratne won the Regional Drilling Engineering Award, while Li was awarded the Regional Formation Evaluation Award.

KAUST team power ahead with alternative energy research

1 min read ·
With the continued growth in Internet of Things applications, deployments, and devices, highly connected, sustainable smart cities are no longer mere concepts or fantastical future dreams; many are already active and are expanding apace.

KAUST Ph.D. student wins 2021 IEEE Sensors Council Technical Achievement Award

1 min read ·
Muhammad Akram Karimi, KAUST Ph.D. student and co-founder of the KAUST-supported startup company Saher Flow Solutions, has been selected as the recipient of the 2021 IEEE Sensors Council Technical Achievement Award in the Field of Sensors – Early Career. The award honors an individual with “outstanding technical contributions within the scope of the IEEE Sensors Council, as documented by publications and patents. It is based on the general quality and originality of contributions.”

Connect the internet of bodies

1 min read ·
A network of body monitoring devices connected using our own tissue could form the basis for the internet of bodies (IoB).

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Smart memory devices that can sense

1 min read ·
In-memory sensing technology will facilitate electronic systems that are faster, smaller and consume less energy.

Ditching sensors for streamlined control of microdevices

1 min read ·
Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.

KAUST students' research wins first place at IEEE AP-S/URSI 2022

1 min read ·
Hanguang Liao and Yiming Yang from the KAUST IMPACT Lab won first place in the 2022 IEEE AP-S Student Design Contest at the 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (IEEE AP-S/URSI 2022), held in July in Denver.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

Wedyan Babatain

Wedyan Babatain is a doctoral candidate of MMH Labs in the Electrical Engineering program at KAUST. She has an interdisciplinary background obtaining her bachelor's degree in Biomedical Engineering from the University of Delaware, USA, in 2017, and her master's degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), KSA, in 2019. Her research interests include sensors, actuators, microfluidics, and flexible and soft electronics for healthcare and environmental applications. She was a finalist at the 2022 SXSW Innovation Awards and has presented her

Tutku Beduk

Tutku Beduk is currently a PhD student at the Sensors Lab, in the Material Science and Engineering Department, MSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Tutku Bedük received her bachelor degree from Middle East Technical University in Chemistry with a High Honor Degree, Turkey in 2016. She also competed her MSc degree in Bilkent University in the field of Chemistry, Turkey in 2018. She is currently a PhD candidate in the Sensor Lab Group under the direction of Prof. Khaled Nabil Salama at King Abdullah University of Science and Technology

Talha Ariff

Talha is studying the use of novel fibre-optic distributed acoustic sensing (DAS) as a tool to help monitor and control anthropogenic acoustic pollution in marine environments.

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Sandeep Surya

Postdoctoral Research Fellow, Sensors
Dr. Sandeep Surya is currently a Post-Doctoral fellow at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Sandeep G. Surya received his Ph.D. degree from IIT Bombay in 2017. He is currently a Post-Doctoral Fellow with Sensors Lab, CEMSE Division, King Abdullah University of Science and Technology. Prior to this, he was a Research Associate with the Microsystems Technology Research Unit, Fondazione Bruno Kessler, Trento, Italy. His forte is in building platforms for different sensor

Mohammad Vaseem

Dr. Mohammad Vaseem is a Research Scientist and Lab Manager in the Integrated Microwaves Packaging Antennas and Circuits Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST) led by Professor Atif Shamim. Research Interests Mohammad's research interests include Tuning Ink chemistry/Ink-formulation based on metal, metal oxide and dielectric materials, Inkjet-Printed devices supported on paper and plastic electronics, Printed transistors, conductors, RFID, and sensors. Book Chapters: • M. Vaseem, Ahmad Umar and Yoon-Bong Hahn, “Zinc Oxide Nanoparticles

Lana Joharji

Lana Joharji completed her master’s degree at the SAMA Labs in the Electrical and Computer Engineering program at KAUST. She received her bachelor's degree in Electrical and Computer Engineering from King Abdulaziz University (KAU), KSA, in 2021. Her research interests include sensors, actuators, flexible and soft electronics for environmental applications. She received the KAUST Graduate Fellowship for her MS/Ph.D. program. She is a student member of the IEEE and Saudi Council of Engineers. After graduating from the SAMA labs, she joined Tufts University, USA, as a PhD student.

Khaled Salama

Professor Salama is a prominent leader in the development of intelligent sensors, biosensors and very large-scale integration (VLSI) architectures for bioimaging and instrumentation. He is one of the founders of the Electrical and Computer Engineering (ECE) Program at KAUST.

Husam N. Alshareef

Professor Husam Alshareef leads research in nanomaterials for energy storage and electronics.

Hommood Alrowais

Hommood Alrowais is a Ph.D. student based in the department of electrical and computer engineering at the Georgia Institute of Technology (Georgia Tech) in Atlanta, Georgia, U.S. Alrowais graduated from KAUST in 2010 as part of the University's first graduating class with a master's degree in electrical engineering. His current research is focused on bio-inspired sensors. "The sensor I am developing is based on the semicircular canal in the inner ear. This tiny organ is responsible for sensing angular rotation in three axes, and it also stabilizes gaze and allows for a smoother reading

Fahad Alam

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Fahad Alam is currently a postdoc at SAMA lab. He received his MSc and MTech from AMU Aligarh, India, in 2009 and 2012, respectively, and PhD from IIT Kanpur, India in 2017. He worked as a Postdoc, in Khalifa University Abu Dhabi from April 2018 to March 2022. He has previously received N-PDF, India, and EU-India (IITK-University of Groningen) exchange fellowship. His research interests include 3D/4D printing and shape memory polymers.

Evangelos Piliouras

Education and early career Evangelos received his integrated master's degree with excellency, from the department of Electrical Engineering and Computer Technology in Patras in 2018. He recently joined King Abdullah University of Science and Technology (KAUST) and he works under the supervision of Prof. Meriem Laleg in the field of estimation. Evangelos did a 3-month internship in Dialog Semiconductor as a software developer, having developed a software automation tool. Research Interests Evangelos' research interests are found in the area of biosensors, embedded system design, modeling and

Christos Sapsanis

Education Profile : - Ph.D. Candidate, Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. - M.Sc., Electrical Engineering (Electrophysics), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2015. - B.Sc., Electrical and Computer Engineering, University of Patras, Greece, 2013. Introduction: Christos Sapsanis obtained his M.Sc. Degree in the Electrical Engineering (EE) Program from King Abdullah University of Science and Technology, under the supervision of Prof. Khaled Nabil Salama. Before joining KAUST, he earned his B.Sc. in Electrical

Chandu V.V. Muralee Gopi

Postdoctoral Research Fellow, Photonics Laboratory
Chandu V.V. Muralee Gopi is a Postdoctoral Fellow at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Science and Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). He received his Master's degree in Interdisciplinary Program in Robotics and a Ph.D. degree in Electrical Engineering from Pusan National University, South Korea, in Feb. 2015 and Feb. 2018 respectively. Over the past 6 years, he has held multiple appointments, within South Korea, and has developed broad expertise and a proven track record in the development of highly

Bashayr Alqahtani

Bashayr Alqahtani is an Electrical and Computer Engineering master’s student at King Abdullah University of Science and Technology, and a student at the KAUST Smart, Advanced Memory devices and Applications (SAMA) Laboratory under the supervision of Professor Nazek El-Atab. Her research interests include the fabrication of physics devices such as sensors for industrial and in-memory sensing applications.

Atif Shamim

Professor Shamim is an accomplished electrical engineer specializing in wireless communication and sensing components and systems. His work aims to improve the performance of wireless communication and sensing systems through low-cost additive manufacturing techniques.

Ahmed Nafe

Ahmed Nafe obtained Ms degree in electrical engineering under the supervision of Prof. Atif Shamim from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2014. He received the B.Sc. degree (Hons.) in electrical engineering from the German University in Cairo (GUC), Cairo, in 2012. His B.Sc. thesis concerned the design and characterization of millimeter-wave antenna arrays for automotive radars with Ulm University, Ulm, Germany. He is currently pursuing a Ph.D. degree in electrical and computer engineering at the University of California at San Diego, La Jolla

Ahmed Alfadhel

Ahmed Alfadhel obtained his both MS degree and PhD degree in Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jurgen Kosel. He received a bachelor's degree in electrical engineering from Rochester Institute of Technology, the US in 2010. Ahmed recently co-founded SONATE, a start-up company that manufactures high-quality smart nanomaterials for medical, electronics and energy applications. Research Interests Ahmed's research interests are in the fields of nano-engineering, wearable sensors, and magnetic micro-systems. His

Abdullah Alshehri

My research focuses on designing efficient interfaces between the analog and digital domains through the development of low-power, high-performance CMOS interface circuits. The interface circuit encompasses key components such as amplifiers, filters, comparators, analog-to-digital converters (ADCs), each of which must be carefully optimized to meet the specific requirements of the target application. By addressing the challenges in analog-digital interfacing, this work aims to contribute to the advancement of integrated circuits and systems that can seamlessly connect real-world analog signals to their digital counterparts, with potential applications in sensor interfaces, data acquisition systems, and mixed-signal electronics. The research involves circuit design, simulation, integration, and experimental evaluation to create novel interface circuit topologies and techniques that improve power efficiency, speed, and overall performance.

Abdulaziz Yaslam

Education 2020.08 - Present M.S. Student in Bioengineering, King Abdullah University of Science and Technology, KSA 2016.07 - 2020.06 B.S. in Biomedical Engineering, University of California at Davis, USA Professional Profile 2021.05 - 2021.12 Master's Student Researcher, KAUST RISC Lab, KAUST, KSA 2016.09 - 2018.06 Teaching Assistant, Electrical Installations and Machines, UNAL, Colombia Honors and Awards ​2018.07 "Best Poster" award in the North Carolina State University - KAUST Summer Internship Areas of Expertise and Research Interests Sensors Human-robot interaction Rehabilitation
<> Computer Vision (95)

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

An Egocentric Computer Vision-based Robotic Wheelchair

Mohammed Kutbi, Assistant Professor at the department of Computer Science and a member of the Artificial Intelligence Unit at Saudi Electronic University (SEU)

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B3 L5 R5209

The emerging need to improve the quality of life for elderly and disabled individuals who rely on wheelchairs for mobility is our motivation for this work. Research on robotics wheelchair covers broad range from motion control, how to control the wheelchair movement, to complete autonomy.

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Indoor 3D Scene Understanding Using Depth Sensors

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KAUST

One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.

Stylistic and spatial disentanglement in GANs

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KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Towards Detailed Understanding of the Visual World

Fahad Khan, Associate Professor at MBZUAI and Linköping University

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B9 L2 R2322

Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Best Business Plan at VIEW 2016

1 min read ·
Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.

Fabian and Victor participate in the annual MSR Summer School!

1 min read ·
Two IVUL Ph.D. students (Fabian Caba and Victor Escorcia) participate in the annual MSR Summer School.

Adel Bibi and Matthias Mueller going to ICVSS 2017

1 min read ·
Adel Bibi and Matthias Mueller have been accepted to participate in the ICVSS (International Computer Vision Summer Schoole) 2017 in Italy. Admission into this summer school is quite competitive in the vision community.​​​

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

1 min read ·
Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

1 min read ·
Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

KAUST prospective student: Qizhou Wang

1 min read ·
Qizhou Wang, 22, is a communication engineering graduate who will join KAUST from the University of Electronic Science and Technology, China. Wang will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate in the KAUST Primalight Lab under the supervision of Professor Andrea Fratalocchi.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

1 min read ·
A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

KAUST prospective student: Xingdi Zhang

1 min read ·
Xingdi Zhang is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Xingdi will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the KAUST High-Performance Visualization Group under the supervision of Professor Markus Hadwiger.

Meet KAUST prospective student: Kilichbek Haydarov

1 min read ·
Kilichbek Haydarov is a graduate student who will join KAUST from Uzbekistan. He will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. Kilichbek obtained his bachelor's degree in computer science and engineering from Sejong University, South Korea.

Meet KAUST prospective student: Kai Yi

1 min read ·
Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Seven IVUL members recognized as Outstanding Reviewers by CVPR 2021

1 min read ·
The 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) has granted Outstanding Reviewer awards to seven members of IVUL: Alejandro Pardo, Ali Thabet, Chen Zhao, Mattia Soldan, Mengmeng Xu, Juan C. Pérez, and Silvio Giancola. CVPR, a premier annual computer vision event that will take place virtually from June 19th to June 25th, is the largest computer vision conference in the world, gathering researchers from academia and industry alike. The peer-review process through which the conference determines acceptance of papers relies on expert reviewers from across the world

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Yuanhao Wang

Yuanhao Wang is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yuanhao Wang obtained his bachelor's degree in Communication Engineering from Beijing University of Posts and Telecommunications in China in 2013. In 2016, he received his master's degree in Integrated Circuits Engineering from Tsinghua University in China. Research Interest Yuanhao’s research interests encompass a wide range of topics in computational photography

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yidan Zheng

Ph.D. Student, Computer Science
Yidan is a MS/PhD student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Education and Early Career Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020. Research Interest Yidan is interested in developing novel computational imaging systems in both hardware and software.

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Xiang Li

Xiang Li is a Postdoc Researcher at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Work Experience Xiang Li joined KAUST as a Postdoc Researcher in Dec 2022. Before that, he was a Postdoc Researcher at the Multimedia and Visual Computing Lab (MMVC) at NYU Tandon and NYU Abu Dhabi from Oct 2019. He received a Bachelor's degree in Remote Sensing Science and Technology from Wuhan University in 2014 and a Ph.D. in Cartography and Geography Information System

Victor Escorcia

Research Scientist committed to innovating AI agents to empower creativity and increase productivity. Passionate about developing cutting-edge AI applications involving video understanding and creative storytelling. I have a wide range of research and engineering interests from on-device AI to General AI. Reach out!

Tarek H. Mahmoud

Areas of Expertise and Research Interests ​Computer Vision Machine Learning Robotics Education Profile ​MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) ​​BS in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia (2016)

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Silvio Giancola

Silvio Giancola is a Research Scientist at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL), part of the Center of Excellence for Generative AI (GenAI).

Shuming Liu

Ph.D. Student, Computer Science
Shuming Liu is a Ph.D. student in the computer science program at the Image and Video Understanding Lab (IVUL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Bernard Ghanem.

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sara Rojas Martinez

Sara Rojas Martinez is a Ph.D. student in the KAUST Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, Sara obtained a master’s degree in Biomedical Engineering from Universidad de Los Andes, Bogotá, Colombia. Research Interests Sara is interested in topics related to the investigation in Artificial Intelligence and Computer Vision. . Education Profile • M.S., Biomedical Engineering, University of Los Andes, Colombia, 2018 • B.E., Artificial Intelligence in Electronics Engineering, University of Los Andes, Colombia, 2017

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Salman Alsubaihi

Salman Alsubaihi was an Electrical and Computer Engineering Master Student in the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Salman Alsubaihi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Saudi Arabia in 2017. In 2019, he received his Master in Electrical Engineering from KAUST. Research Interests ​ Salman is focusing on understanding deep neural

Rui Li

Rui Li is an Electrical Engineering Ph.D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Rui Li obtained bachelor of engineering degree in Telecommunication Engineering from Xidian University in China in 2013. After that in 2016, he received his master degree in Electrical Engineering from University of Science and Technology of China in 2016. Rui started his career life as a Research Assistant at State

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Qizhou Wang

Qizhou Wang is an M.S./Ph.D. candidate in the KAUST Primalight research group under the supervision of Professor Andrea Fratalocchi. Before joining KAUST, Qizhou obtained a bachelor's degree in communication engineering from the University of Electronic Science and Technology, China. Research Interests Qizhou's research interests include machine learning, optimization theories and computer vision.

Qian Wang

Ph.D. Student, Computer Science
Qian Wang is a MS/Ph.D. student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Wonka in his research group. Education Qian Wang graduated with great distinction from Wuhan University with Bachelor's Degree. Research Interest Research interest of Qian Wang is Computer Vision.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Han

Ph.D. Student, Computer Science
Peng Han is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Prior to that, he was a Research Associate in the School of Computer Science and Engineering, Nanyang Technological University (NTU) under the supervision of Prof. Aixin Sun, Singapore. He received his M.E. in Computer Science from Renmin University of China (RUC) at BDAI lab (leaded by Prof. Ji-rong Wen) in 2017. Research Interests ​Peng's research interests

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Motasem Alfarra

Motasem Alfarra is an Electrical and Computer Engineering Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Mojtaba Alshams

Education and Early Career Earned a BS in Electrical Engineering with 2nd honor from King Fahd University of Petroleum & Minerals where the degree specialization was more oriented around communication, electronics and embedded systems. Had about three years of high professional experience and empowered leadership in industrial electrical and instrumentation engineering works in district cooling plant construction project. Currently pursuing MS degree in Electrical & Computer Engineering at KAUST with a thesis in distributing ML algorithms across hardware accelerators. Simultaneously, engaging
KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Mengmeng Xu (Frost)

Mengmeng Xu (Frost) was an Electrical and Computer Engineering PhD Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. He is currently continuing his Ph.D. Education and Early Career Mengmeng obtained his bachelor's degree in Optics and Optical Sciences from Zhejiang University in China in 2017 and his Master's Degree from KAUST under the supervision of Professor Bernard Ghanem. Research Interests He is mainly focusing on problems that arise in computer vision, including

Matthias Mueller

Matthias Mueller was an Electrical and Computer Engineering Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at Visual Computing Center (VCC) under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Matthias graduated with a B.Sc. in Electrical Engineering and Math Minor from Texas A&M University in 2011. After graduation, he joined P+Z Engineering in Munich, Germany as an Electrical Engineer and worked for 3 years on the development of mild-hybrid electric machines at BMW. In 2014, he started his M

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Kilichbek Haydarov

Ph.D. Student, Computer Science
Kilichbek Haydarov is a M.S./Ph.D. candidate at the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. Before joining KAUST, Kilichbek obtained a bachelor's degree in computer science and engineering from Sejong University, South Korea. Education and early career Kilichbek Haydarov obtained his bachelor’s degree from Sejong University in 2020 February (Seoul, South Korea). His major was Computer Science and Engineering. During his undergraduate years, he was working in Intelligent Media Lab and focusing on anomaly detection and action

Khaled Abu-Jbara

Khaled Abu-Jbara was a Ph. D. student in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Khaled Abu Jbara received his bachelor degree in Electrical Engineering, with Distinction, First Class Honors, from Kuwait University (KUNIV), Kuwait in August 2011. He worked as an Administrative Engineer and Teacher Assistant at Kuwait University, Electrical Engineering Department. After that, he joined King Abdullah University of Science and Technology (KAUST) as a

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Juan C. Pérez

Juan C. Pérez was a Ph.D. Candidate in the computer science program under Professor Bernard Ghanem's supervision at the Image and Video Understanding Lab at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, Juan obtained a master's degree and a bachelor's degree ( cum laude) in biomedical engineering from Universidad de Los Andes, Colombia.

Juan Pablo Peña (Student)

Juan Pablo Peña is part of the Visiting Students Research Program at King Abdullah University of Science and Technology (KAUST). During his Internship, he is conducting his research at the Sensors Lab, in the Electrical Engineering Department, CEMSE, KAUST. Education and Early Career Juan Pablo Peña graduated from Mecatronichs Engineering B.S. at the National Autonomous University of Mexico, Mexico City on May 2019. Additionally, on 2019 summer he worked on an augmented reality simulation for visual impairments created during his research internship at Ryerson University, Canada. Similarly, on

Jia-Hong Huang

Jia-Hong Huang was a Master's student in the Electrical and Computer Engineering Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Career Jia-Hong earned a double major bachelor's degree in Statistics and Mathematical Science from National Chengchi University in Taiwan in 2014. He received his master's degree in Mechanical Engineering from National Taiwan University in 2016. After that, he joined KAUST and received his second master's degree in Electrical Engineering in 2018

Jean Lahoud

Jean Lahoud was a Ph. D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Jean Lahoud received his Bachelor of Engineering in Mechanical Engineering from Notre Dame University in Lebanon in 2012. In 2014, he obtained his Master of Engineering degree in the same field from American University of Beirut in Lebanon. Before joining KAUST, Jean was a product manager at Murex systems in Lebanon. Research Interest Jean Lahoud is interested in 3​D scene understanding using RGB-D sensors. Honors and Awards In

Jesus Zarzar

Jesus Zarzar was an Electrical and Computer Engineering PhD student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Jesus obtained his bachelor degree in Electrical and Computer Engineering from UT Austin, United States in 2017 before obtaining his Master's Degree from KAUST under the supervision of Professor Bernard Ghanem. Publications Education Profile ​B.S., Electrical and Computer Engineering, UT Austin, Austin, United States, 2017

Ivan Skorokhodov

Ph.D. Student, Computer Science
Ivan Skorokhodov is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Peter Wonka at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). His research interests include such areas of deep learning as generative models, hypernetworks and neural rendering. Ivan Skorokhodov has obtained his Ph.D degree and graduated in March 2023. Education and Early Career Ivan obtained his bachelor's degree in Management from the Moscow Engineering Physics Institute in 2014. He continued his studies there and received his master's degree

Humam Alwassel

Ph.D. Student, Computer Science
Humam Alwassel is a Computer Science Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor degree with double major in Computer Science and Mathematics from Cornell University in New York, USA in 2016. After that, he joined KAUST for the MS/PhD program and received his master degree in Computer Science in 2018. Research Interest He’s currently focused on the development of novel computer vision techniques for video

Hasan Abed Al Kader Hammoud

Hasan is an Electrical and Computer Engineering MS/Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Hasan obtained his bachelor's degree in Electrical and Computer Engineering from American University of Beirut in 2020. He joined KAUST in 2020 to pursue his MS and PhD degrees. Research Interest Deep Learning Machine Learning Computer Vision Publications Rapid Adaptation in Online Continual Learning: Are We

Hajer Abbas

Visiting Student (former), Electrical and Computer Engineering
Visiting Student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Habib Slim

Ph.D. Student, Computer Science
Habib Slim is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Education: M.Sc Data Science, Grenoble Institute of Technology, France, 2021 M.Eng Applied Mathematics and Computer Science, Grenoble Institute of Technology, France, 2021

Gordon Guocheng Qian

Guocheng Qian is currently working towards an MS/Ph.D. degree in the Department of Computer Science at King Abdullah University of Science and Technology (KAUST). He is part of the Image and Video Understanding Lab (IVUL), supervised by Prof. Bernard Ghanem. During his Ph.D. studies, he interned at several leading institutions including Snap Research, Meta Reality Lab, and MEGVII research. His primary research interest lies in 3D vision. Education and Early Career Guocheng obtained his bachelor's degree in Engineering with first-class honors from Xi’an Jiaotong University (XJTU) in China in

Guohao Li

Ph.D. Student, Computer Science
Guohao Li is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Guohao Li obtained a Bachelor’s Degree in Communication Engineering from Harbin Institute of Technology in China in 2015. In 2018, he received his Master Degree in Communication and Information System from Chinese Academy of Science in China. Research Interest His primary research interest is in Computer Vision and Deep Learning. Publications Education Profile M.Sc. in Communication and Information System, Chinese Academy of

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that

Frankly Toro

Frankly Toro is a Ms/Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career He is a graduate of National University of Colombia where he received a B.S. degree in Mechatronics. Education Profile B.S., Mechatronics , National University of Colombia, 2020

Fabian Heilbron

Fabian Caba Heilbron was a Ph.D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). He is currently a Research Scientist at Adobe Research in San Jose, California. Education and Early Career Fabian has a Bachelor’s Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia, 2011. In 2013, he received his Master Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia. He joined KAUST as a Ph.D. candidate and received his

Chen Zhao

Chen Zhao is a Research Scientist at KAUST, and Lead of the Video Understanding Theme in the Image and Video Understanding Lab. She received her Ph.D. degree from Peking University, China in 2016. She studied in University of Washington, US from 2012 to 2013, and at the National Institute of Informatics, Japan in 2016. She has published 40+ papers on representative journals and conferences such as TPAMI, CVPR, ICCV, ECCV, and has received over 3000 citations according to Google Scholar.

Cheng Zheng

Cheng Zheng is a Software Engineer Consultant working with Professor Bernard Ghanem in the Image and Video Understanding Lab (IVUL) in the Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Cheng Zheng received his Bachelor's degree in Computer Science from Beijing University of Posts and Telecommunications (BUPT), China, in 2009. He received his Master's degree from the University of Chinese Academy of Sciences (UCAS), China, In 2012. Before joining KAUST, in 2012, he worked as a software engineer at NetEase Games in China. In

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Boris Radovic

Boris Radovic is a Ph.D. candidate in the computer science program, under the supervision of Professors Marco Canini and Veljko Pejović, and is a member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab. He holds a Bachelor of Science (B.Sc.) in Computer Science and a Master of Science (M.Sc.) in Data Science, both obtained from the Faculty of Computer and Information Science at the University of Ljubljana, Slovenia. During his academic journey, Boris gained experience across diverse computer science domains through his long-term collaboration with the Laboratory

Ali Thabet

Ali Thabet is a Research Scientist at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), working with Professor Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL). Education and Early Career Ali obtained a bachelor of engineering degree in Electrical Engineering, from the American University of Beirut in Beirut, Lebanon in 2006. He holds a ​Ph.D. in Computer Science from the University of Dundee in Dundee, UK. He joined KAUST as a Postdoctoral Fellow in the Visual Computing Center (VCC) from 2012 to 2014. After that, he worked as

Alejandro Pardo

Alejandro Pardo is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) program under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Alejandro has an Engineer’s degree in Artificial Intelligence in both Electronics and Biomedical Engineering from the University of Los Andes in Colombia in 2017. He continued his studies at the University of Los Andes and received his Master’s Degree in Biomedical Engineering in 2018. He visited KAUST as a

Ahmed S. Abdelreheem

Ahmed Abdelreheem is a Computer Science MS. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Ahmed Abdelreheem is a graduate of Cairo University, where he received a B.S. degree in Computer Engineering in 2019. Research Interest Ahmed focuses on Computer Vision and Machine Learning. Education Profile BSc., Computer Engineering, Cairo University, Egypt, 2019

Abdurauf Abdurakhmanov

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abdullah Hamdi

Abdullah Hamdi is a Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career In 2016, Abdullah Hamdi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. After that, he joined KAUST to proceed in the same field of study and received his master degree in 2017 in Computer Vision track. Research Interest Machine Learning Robustness of Deep Learning Computer Vision
<> electrical engineering (91)

Blue LED material extended to Green, Yellow, and now Red

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KAUST

In this seminar, the science of MOCVD, the material science of InGaN, and the new-born InGaN-based red LED performance will be discussed. The three primary colors in light are RGB. Green and blue LEDs have been realized by using InGaN active region. The current red LEDs are based on AlGaAs or InGaP as the active region. If we can realize red LEDs by InGaN, it is possible to integrate RGB LEDs in a wafer. Such RGB integration is a breakthrough to develop the next displays, so-called, micro-LED displays that are the next after the OLED displays, and functional LED lightings.

Windows could take the heat

1 min read ·
Generators embedded in windows produce electricity from the temperature differential between an air-conditioned interior and a sunny outside world.

A different kind of light

1 min read ·
The discovery of the incandescent light bulb has transformed human existence. New LED technologies promise to be the next step forward.

Targeting cancer cells with tiny magnetic wires

1 min read ·
Magnetic nanowires with weak magnetic fields and low frequencies can destroy cancer cells without generating heat.

Signal noise annoys no more

1 min read ·
Identifying noise in communication signals helps to filter out glitches and improve transmission quality.

Better routing for wireless networks

1 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

Perfect absorption graphene style

1 min read ·
A tiny device broadens the bandwith to enable absorption with wide-reaching potential for electrical engineering.

Sensors for a light touch

1 min read ·
Biocompatible tactile sensors based on magnetic hair-like structures enable new applications for touch sensors.

Miniature flexible sensor to detect heart disease

1 min read ·
A new biosensor made of laser-etched electrodes on a gold-coated polymer may provide an effective and cost-efficient way to assess heart disease risk.

Smart technology shows where we're at

1 min read ·
A combination of state-of-the-art sensors and standardized data analysis will transform research for tracking animals and humans.

Wireless devices tune-in to cloud power

1 min read ·
Next-generation mobile networks can use cloud computing algorithms to manage the increasingly high data demands of users.

Sneaky losses solved for resistive memory

1 min read ·
A data readout scheme achieves an unprecedented reduction in power consumption for a promising high-performance resistive memory architecture.

Tweaking the perfect recipe

1 min read ·
Adjusting the cooking time is all it takes to tune the magnetic properties of these multi-functional iron nanowires.

Gentle sensors for diagnosing brain disorders

1 min read ·
New sensor design paves the way for safer and more effective brain monitoring.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

Driven to diffraction

1 min read ·
Optical elements that diffract light could lead to smaller and more powerful cameras.

Blind matchmaking for more efficient wireless networks

1 min read ·
Autonomously pairing network users could expand the capability of the next generation of wireless networks

Stretching toward energy efficiency

1 min read ·
Deformable thermoelectric materials add a new twist to the design of energy-scavenging devices.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Keeping the heat out

1 min read ·
Insights into the thermal behavior of metal-nitride nanowires could open new avenues in optical electronics.

A firefly's flash inspires new nanolaser light

1 min read ·
Synchronized emissions from innovative on-chip lasers create possibilities for inexpensive artificial neural networks.

No strings attached for underwater video system

1 min read ·
An underwater wireless optical communications system for streaming high-quality, live video.

Bringing signals into phase

1 min read ·
A novel type of electronic component made from a blend of polymer materials could enable more effective circuitry.

Semiconductors with an aligned interface

1 min read ·
Efficiency gains come from tuning the properties of semiconducting materials by combining layers of different composition.

Taking control at the junction

1 min read ·
Fine tuning the composition of nitride alloys can further the development of optical and electronic interface devices.

Making waves for ultrahigh definition displays

1 min read ·
Wavy transistors that vertically gain width without increasing their on-chip footprint could drive future flexible displays.

Marine exploration sensing with light and sound

1 min read ·
Unveiling new strategies to improve future wireless underwater sensing networks for marine research and communication.

Building crystals on a very hot surface

1 min read ·
An innovative chemical reactor for depositing semiconductors at very high temperatures draws on the strength of Saudi Arabia’s chemical industry.

Smart skin for flexible monitoring

1 min read ·
An electronic tag that stretches and flexes while it records location and environmental data can monitor marine animals in their natural habitat.

Controlling the crystal structure of gallium oxide

1 min read ·
Precise control of the atomic structure of gallium oxide layers improves the development of high-power electronic devices.

Hussain is taking a flexible approach

1 min read ·
“The theme of our research is how we can empower humanity with technology,” said Muhammad Mustafa Hussain, professor of electrical engineering at KAUST. Using the skills from his time in industry, and the support he is granted by KAUST, Hussain creates flexible, wireless electronics to inexpensively turn everyday objects into smart devices. When Hussain had his novel computer chip technology licensed by Intel Corporation before he had even graduated from his Ph.D., he soon found himself a rewarding job in the semiconductor chip industry. But Hussain harbored the desire to one day go into

Faculty Discuss their Career Journeys and Professional Development

1 min read ·
Join our first session with DR. JÜRGEN KOSEL - Associate Professor of Electrical Engineering CEMSE - PI of Sensing, Magnetism and Microsystems Group This session will be followed by a Q&A discussion. Students across all divisions are welcome. Light snacks will be provided for those who are registered. Register via the Student Life Forum by clicking here Date: Wednesday, Sept. 19th Time: 4:30 – 5:30 pm Location: Student Center, Level 4, Fellowship Hall For more information please contact Office of Professional Development - Professional.Development@KAUST.EDU.SA

Alumni Focus: Wail Bamhair (M.S. '10, electrical engineering)

1 min read ·
Wail Khalid Bamhair (M.S. '10, electrical engineering - nanotechnology) hails from Jeddah and is currently the CEO of TAQNIA Energy, the energy arm of Saudi Investment and Development. Bamhair received his master's degree from KAUST in 2010, the year the University's first class of graduating students were awarded their degrees.

Brighter blue lights for faster communication

1 min read ·
High-performance blue-light-emitting diodes could boost white-light, high-speed data transmission.

Lighting the way to the underwater Internet of things

1 min read ·
An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

3D-printed cube dials into energy harvesting

1 min read ·
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.

Marine sensor gets to grips with salt

1 min read ·
Laser-induced graphene formation creates tailor-made sensors for monitoring ocean creatures and ecosystems.

KAUST to launch first virtual fabrication lab

1 min read ·
The vFabLab™ platform, short for Virtual Fabrication Lab, will be presented on April 24th during a webinar organized and hosted by the IEEE Electron Devices Society.

MOFs can sense and sort troublesome gases

1 min read ·
Fluorinated metal-organic frameworks make excellent materials for selective sensing and removal of toxic gases.

Conducting Fab lessons in a virtual clean room

1 min read ·
A virtual platform makes learning how to fabricate a semiconductor device easier, safer and more interactive.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Marine Skin dives deeper for better monitoring

1 min read ·
A pioneering tagging system that monitors the movement and local environment of sea animals reaches deeper depths and higher sensitivities.

Smart pill bottle keeps drugs safe

1 min read ·
Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.

Flat lenses with a twist!

1 min read ·
Overlaying two film layers patterned with a nanoscale array can manipulate the propagation of light to create a powerful ultrathin lens.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

A cool alternative to air conditioning

1 min read ·
An inexpensive passive cooling technology could be used to cool buildings in cities, reducing energy consumption.

Baby steps for transparent electronics

1 min read ·
Stretchy, see-through, silver nanowire sheet combines optical transparency with excellent electrical conductivity.

Flexing for success from Saudi Arabia to Silicon Valley

1 min read ·
Asrar Damdam is setting up her own biotech company in Silicon Valley while pursuing a Ph.D. at KAUST

Magnetic skin ensures the force is with you

1 min read ·
A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

Flexible thinking on silicon solar cells

1 min read ·
Combining silicon with a highly elastic polymer backing produces solar cells that have record-breaking stretchability and high efficiency.

Patterned optical chips keep secrets perfectly safe

1 min read ·
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.

Internet of underground things to reveal buried vistas

1 min read ·
Pinpoint mesh of smart underground objects could give real-time 3D readout of fossil fuel reserves.

A pure twist on optical data transfer

1 min read ·
The finding that light can be twisted very precisely may offer fresh options for communications infrastructure.

Sensor cube helps keep fish farming afloat

1 min read ·
A self-powered water quality sensor could help fish farmers to monitor pollution in their ponds remotely.

Sensing a heart attack before it strikes

1 min read ·
Nanomaterial-based electronic device monitors a key heart health biomarker.

Light sensors detect larval pests munching on date palms

1 min read ·
Optical fibers wrapped around date palm trunks could help detect this tree’s most destructive pest early enough to save it.

Early warning sensor sniffs out cities' harmful gas

1 min read ·
An integrated detector device could form the basis of a distributed air-quality sensor network.

Iron nanorobots go undercover

1 min read ·
Customizable magnetic iron nanowires pinpoint and track the movements of target cells.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Pure red LEDs fulfill a primary goal

1 min read ·
First high-intensity, low-voltage red LEDs made from nitride semiconductors.

Meet KAUST prospective student: Adilya Bakambekova

1 min read ·
She loves watching films not for the plot, but in order to rethink her beliefs. She is 22, she graduates from the School of Engineering in the capital. Adilya Bakambekova may seem ordinary: she loves walking in the park, powerlifting, taking care of her five-year-old wiener dog.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.

Red-light LEDs for next-generation displays

1 min read ·
Novel red LEDs are more temperature stable than those made using the conventional semiconductor of choice.

Membrane makeover makes face mask reusable

1 min read ·
A polyimide membrane with regularly sized and spaced pores turns the N95 respirator into a reusable mask for protection against COVID-19.

To Boldly Go Where No Sensor Has Gone Before: The Movement to Place IoT in Radical New Spaces

1 min read ·
The rapid growth of the world’s population demands more natural resources, food, and space. World renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. The framework of X-IoT consists of three major categories as shown in Figure 1. The first one is the

Standing the test of time with a perfect partner

1 min read ·
Identifying the ideal co-catalyst can significantly extend the working lifetime of solar fuel–generating photocatalysts.

A fresh sense of possibility

1 min read ·
A multisensory graphene-based skin can sense in extreme environments where other sensors cannot be used.

Sheer protection from electromagnetic radiation

1 min read ·
A printable ink that is both conductive and transparent can also block radio waves.

Sensors get a laser shape up

1 min read ·
Laser writing breathes life into high-performance sensing platforms.

Intelligent surfaces signal better coverage

1 min read ·
A mathematical model shows specialized reflective panels could be deployed on a large scale to enhance communication networks in urban areas.

KAUST Discovery features "Optoelectronic devices that emit warm and cool white light"

1 min read ·
A single semiconducting material can produce white light by emitting light across the visible spectrum.

Broadening horizons for people with quadriplegia

1 min read ·
An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Breaking records like baking bread

1 min read ·
Researchers have made unparalleled ultrawide-bandgap semiconductors through temperature and timing, just like baking bread.

Brain-on-a-chip would need little training

1 min read ·
A neural network that mimics the biology of the brain can be loaded onto a microchip for faster and more efficient artificial intelligence.

Making the shift from blue to red for better LEDs

1 min read ·
Pure red-light micrometer-scale emitting devices made from a nitride semiconductor reaches excellent efficiency.

KAUST Ph.D. candidate wins first place at IEEE SSD’21 PhD Students Contest

1 min read ·
KAUST Ph.D. candidate Mohamed Abdelwaheb Bahloul recently won first place at the IEEE International Multi-Conference on Systems, Signals and Devices 2021 (SSD’21) PhD Students Contest. The contest provided a showcase for students’ original work related to electrical engineering and information technology.

KAUST team power ahead with alternative energy research

1 min read ·
With the continued growth in Internet of Things applications, deployments, and devices, highly connected, sustainable smart cities are no longer mere concepts or fantastical future dreams; many are already active and are expanding apace.

Plant probes make sense for sustainable farming

1 min read ·
Ultrathin needles for probing plants could help keep crop health in check.

Divided attention could ease wireless congestion

1 min read ·
Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Full color LEDs cut down to size

1 min read ·
Tiny light-emitting devices that can create all the colors in the rainbow are essential for the next generation of phones and screens.

Airborne antennas to address electro-magnetic fields exposure concerns

1 min read ·
Movable airborne antennas receiving cellphone signals could reduce EMF exposure while offering higher data transmission speed and using less power.

Welcoming composite inks into the fold

1 min read ·
A screen-printing approach to creating foldable circuits could make many functional devices easier and cheaper to mass produce.

Connect the internet of bodies

1 min read ·
A network of body monitoring devices connected using our own tissue could form the basis for the internet of bodies (IoB).

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Smart memory devices that can sense

1 min read ·
In-memory sensing technology will facilitate electronic systems that are faster, smaller and consume less energy.

Fiber glow brings more go for underwater communications

1 min read ·
KAUST scientists have developed an optical-based wireless network system that has significant potential for faster underwater optical communications.

Ditching sensors for streamlined control of microdevices

1 min read ·
Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.

Ibrahim Almuqbil

Internship in King Abdullah University of Science and Technology (KAUST)

Aminta Naidili Castillo Robles

Ahmad Bin Afzal

Afzal received his B.Tech. degree in Electrical Engineering from Zakir Husain College of Engineering and Technology in 2023. He is an ECE Master's student in the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou at King Abdullah University of Science and Technology (KAUST). Education Profile Bachelor of Technology in Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, India.
<> bioinformatics (89)
AI tool maps hidden links between diseases

AI tool maps hidden links between diseases

1 min read ·
Understanding how diseases trigger one another could lead to better treatments, prevention strategies and improved genetic risk prediction.

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Bio-Hackathon MENA 2023

Bio-Hackathon MENA 2023

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KAUST Hotel

Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.

Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

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B3 L5 R5220

We propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Building on a static latent variable specification, our model learns (independent) stochastic factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space. This approach provides actionable modeling through the controlled generation of time-series data for novel input combinations (or perturbations). Additionally, we propose a flexible approach for quantifying uncertainties, leveraging a quantile regression formulation.

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

1 min read ·
Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Computing solutions for biological problems

1 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Robert Hoehndorf promoted to associate professor at KAUST

1 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

1 min read ·
Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

MS Thesis Defense: Rare variant collapsing analysis on UK Biobank by Yang Liu

1 min read ·
Yang Liu, M.S./Ph.D. candidate in the Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended her thesis "Rare variant collapsing analysis on UK Biobank".

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

1 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

MS Thesis Defense | Learning 3D structures for protein function prediction

1 min read ·
Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

A new approach to rare disease diagnosis

1 min read ·
An innovative AI-based tool leverages varied symptom descriptions, along with evidence from the scientific literature and genomic datasets, to pinpoint disease-associated gene variants.

AI tool predicts function of unknown proteins

1 min read ·
Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.

Zhirun Li

Zhirun Li is a M.S./Ph.D. student at King Abdullah University of Science and Technology (KAUST). He obtained bachelor degree of Optoelectronic Information Science and Engineering from University of Electronic Science and Technology of China (UESTC) in 2019. Education Profile M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2020 - present B Eng. in Optoelectronic Information Science and Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2015 - Jun. 2019
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Zainab A. Homoud

M.S. Student, Bioengineering
Research Interest Studying : Brain time series, fMRI, EEG, Signal Processing and Machine Learning for better Mental Disease Diagnosis and Consciousness Study. Previous Education Undergraduate study (B.S) in Biomedical Engineering and Health Science at the Joint program of North Carolina State University and UNC Chapel Hill.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Tilman Hinnerichs

Visiting Student, Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Rund Tawfiq

Ph.D. Student, Bioengineering
Research Interests My research interests include bioinformatics and using computational methods for data analysis in biology. I am interested in working on biological questions that require multi-omics data integration. I am also interested in neuropharmacology and the molecular biology underlying neuropsychiatric disorders. Professional Profile 2015-2019: Bachelor of Science in Biology, Psychology and Neuroscience; University of Massachusetts - Amherst 2019-2021: Master of Science in Bioscience; King Abdullah University of Science and Technology 2021-present: Ph.D. student; King Abdullah

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Rahaf Shiqdar

Visiting Student, Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Olga Mashkova

Ph.D. Student, Computer Science
PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Kosuke Goto

Research Interests ​Development of bioinformatics tools and databases for molecular evolution, population genetics, metagenomics. Professional Profile ​2015-present: Technical Specialist, KAUST, Thuwal, Saudi Arabia Education Profile ​M.Sc., Applied Biological Science, Tokyo University of Science, Chiba, Japan, 1993 B.Sc., Physics, Tokyo University of Science, Tokyo, Japan, 1991

Kexin Niu

Ph.D. Student, Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical

Hajime Ohyanagi

Research Interests ​My original question in biology is the Origin and History of Eukaryotes. I also love to understand the possibility of Genomic Breeding in the imminent global food security issue. To these ends, currently, I aim to determine the biological significance in big data of marine and crop genomics, by the means of diverse bioinformatics methodologies. Professional Profile 2015-present: Technical Specialist, KAUST, Thuwal, Saudi Arabia 2015-present: Visiting Research Scientist, National Agriculture and Food Research Organization, Tsukuba, Japan 2013-present: Visiting Research

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Fernando Patricio Zhapa-Camacho

Ph.D. Student, Computer Science
Fernando Patricio Zhapa Camacho is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST, Fernando obtained a bachelor's degree in Information Technology from Yachay Tech University, Ecuador. Research Interests Fernando’s research interests include knowledge representation and reasoning, machine learning and bioinformatics. He is interested in artificial intelligence and logic integration with applications in biology. Professional Profile: • 2020 to present: Graduate Student, KAUST, Thuwal, Saudi Arabia. •

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as

Christophe Van Neste

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Christophe Van Neste is a Postdoctoral Fellow under the supervision of Professor Xin Gao at the Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests ​My topic interests are centered around cancer and development, specifically related to the embryonal cancer neuroblastoma and how it links to the normal development of the sympathetic neuronal system. My focus there lies on replicative stress and secondary DNA structures such as G-quadruplexes and how that impacts development or cancer stem cell maintenance. My

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Ahmed Abbas

Ahmed Abbas obtained his Ph.D. degree under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Ahmed is an Assistant Professor at Ain Shams University, Egypt. Research Interests Ahmed's research interests included Bioinformatics, NMR protein structure determination. Selected Publications 1. Carlo Cannistraci, Ahmed Abbas, and Xin Gao. (2015). “Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra”. Scientific Reports (Nature

Abeer Almutari

M.S. Student, Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student, Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.
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PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

On Studying Computer Science - 2020-03-16

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KAUST

In this talk, I will discuss a wide range of ideas on studying computer science and doing research in computer science. Peter Wonka is the program chair of the computer science program. His research interests are deep learning, computer graphics, computer vision, and remote sensing.

Situational Awareness and Decision-Making Support for Crowd Management in Mina, A Grand Challenge Project Funded by the Ministry of Education

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KAUST

Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.

On Studying Computer Science - 2020-09-07

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KAUST

The talk will be pre-recorded and a youtube link will be provided to seminar participants. During the seminar time, Prof. Wonka will mainly answer some questions from the audience.

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

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B5 L5 R5220

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Choosing the right scheme for green communications

1 min read ·
Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Divide and conquer pattern searching

1 min read ·
Searching for recurring patterns in network systems has become a fundamental part of research and discovery in fields as diverse as biology and social media. KAUST researchers have developed a pattern or graph-mining framework that promises to significantly speed up searches on massive network data sets. “A graph is a data structure that models complex relationships among objects,” explained Panagiotis Kalnis, leader of the research team from the KAUST Extreme Computing Research Center. “Graphs are widely used in many modern applications, including social networks, biological networks like

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Streamlining space structures

1 min read ·
A new computational framework marries ideas from computer graphics with civil engineering to reduce times and costs of constructing frame-based structures.

Xiangliang Zhang joins CBRC

1 min read ·
Xiangliang Zhang, KAUST associate professor of computer science and principal investigator of the Machine Intelligence & Knowledge Engineering Lab, joined the Computational Bioscience Research Center (CBRC) on July 1, 2018. In CBRC, Xiangliang will work on the problems of modeling biological networks using machine learning models.

Polymers offer a better view

1 min read ·
A window opens for analyzing the distribution of small molecules in biological and medical tissue samples.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Symbol of change for AI development

1 min read ·
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

Algorithm turns cancer gene discovery on its head

1 min read ·
A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Peeling back the layers of deep machine learning

1 min read ·
A layer-based approach raises the efficiency of training artificial intelligence models.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

First 3D Visualizations of SARS-CoV-2 Cryo-EM Tomography Data

1 min read ·
Together with our partners from Tsinghua University and Nanographics, our group has succeeded in depicting the first 3D visualizations of Cryo-EM microscopy tomography directly from the data. We are advancing the technology to allow for clear, noise-free visualization of this challenging data modality. Once we succeed with that our next challenge is to integrate such 3D visualization into the structural modeling process. Stay tuned!

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Divided attention could ease wireless congestion

1 min read ·
Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

MS Thesis Defense: Embedding Ontologies using Category Theory Semantics by Fernando Zhapa Camacho

1 min read ·
Fernando Patricio Zhapa Camacho an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended his thesis "Embedding Ontologies using Category Theory Semantics".

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Watching viscous flow, but faster

1 min read ·
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.

Alumni in focus: Abdullah AlGziwi

1 min read ·
Abdullah AlGziwi, a visiting student under Professor Xin Gao's supervision, was recently awarded the “Second Place Grand Award” at the Saudi National Olympiad for Scientific Creativity. He presented his research project on “A Novel exosome-encapsulated-miRNA-based machine learning model for multiple cancer prediction and prognosis”.

MS Thesis Defense | Learning 3D structures for protein function prediction

1 min read ·
Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

PhD Dissertation Defense | Ontology design patterns and methods for integrating phenotype ontologies

1 min read ·
CBRC PhD student Sarah Alghamdi successfully defended her dissertation last week. She has been an essential member of the Bio-Ontology Research Group (BORG) since 2015.Her research interests include the applications of artificial intelligence and statistical methods to genomics and healthcare.

PhD Dissertation Defense | Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

1 min read ·
CBRC PhD Student Azza Althagafi successfully defends her dissertation under the supervision of Associate Professor Robert Hoehndorf. She has been a member of the Bio-Ontology Research Group (BORG) since 2016. Her research interest lies in the crossroads of computer science and biology, leveraging AI techniques to explore the human genome's intricacies and its link to various diseases.

Prof. Xin Gao assumes the position of CEMSE CS Program Chair

1 min read ·
Prof. Xin Gao has assumed the role of CEMSE CS Program Chair, marking a significant development for our academic community.

Former CBRC PhD Student Ibrahim Alabdulmohsin Highlighted for AI Contributions on Saudi National TV

1 min read ·
Former CBRC PhD student Dr. Ibrahim Alabdulmohsin, now a senior research scientist at Google DeepMind in Zurich, was featured by the Josoor Program on Saudi National TV. The program, meaning "Bridges" in Arabic, highlighted Dr. Alabdulmohsin's journey from his education at the University of Nebraska, Lincoln, and Stanford University to his leadership roles at Aramco's Advanced Analytics team. After graduating from KAUST under the supervision of Prof. Xin Gao, he joined DeepMind in 2019, contributing to groundbreaking AI research. The 24-minute episode explores his career and contributions to the field. Dr. Alabdulmohsin's commitment to advancing AI underscores his pivotal role in shaping the future of technology.

Razen Al Harbi

Razen Al Harbi obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interests Razen's research interests included ​Large scale data management with an emphasis on managing and querying large-scale graphs. Selected Publications Ali H. Dogru, Larry S. K. Fung, Usuf M, Tareq M. Al-Shaalan, Jorge A. Pita, K. Hemanthkumar, H. J. Su, James C. Tan, H. Hoy, W. T. Dreiman, W. A. Hahn, R. Al-Harbi, A. Al-Youbi, N. M. Al-Zamel, M. Mezghani “A

Elisa Chiapponi

I am currently a PhD student in Digital Security enrolled in the Sorbonne Université. I work on my research project at EURECOM and Amadeus, under the supervision of prof. Marc Dacier at KAUST My areas of interest include Network and Web security. My thesis focuses on the analysis and mitigation of the new generation of botnets, in particular the ones performing web scraping. My goal is to find practical means to defeat them. The starting point of my project is understanding the ecosystem of scraping bots. This consists of identifying the actors behind this business and their motivations
<> robotics (70)

KAUST developing robotic system to improve date palm harvesting

1 min read ·
Combination of AI and robotics positions Saudi Arabia to transform date farming.

Sense of Touch: from Haptics to Robotic Tactile Sensing

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B9 L2 R2325

This talk explores the intersection of haptics, human touch perception, and robotic tactile sensing, covering principles of human touch, traditional and emerging robotic tactile sensors, and their applications in human-robot interaction, including a case study on tactile gloves for breast lump detection.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

The route to chaos in routing games: Population increase drives period-doubling instability and chaos with Price of Anarchy equal to one

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B1 L3 R3119

We study a simple learning dynamic model of routing (congestion) games to explore the effects of increasing the total demand on system performance. We focus on the most benign setting, non-atomic routing games with two parallel edges of linear cost, where all agents evolve using Multiplicative Weights Updates with a fixed learning rate.

An Egocentric Computer Vision-based Robotic Wheelchair

Mohammed Kutbi, Assistant Professor at the department of Computer Science and a member of the Artificial Intelligence Unit at Saudi Electronic University (SEU)

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B3 L5 R5209

The emerging need to improve the quality of life for elderly and disabled individuals who rely on wheelchairs for mobility is our motivation for this work. Research on robotics wheelchair covers broad range from motion control, how to control the wheelchair movement, to complete autonomy.

Towards Collaboration of Multiple Intelligent Robots and Human Supervision

Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester

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B2 L5 R5209

As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.

Distributed Robotics

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KAUST

This talk presents an overview of challenges, state-of the-art, and applications for distributed robotic systems. In distributed robotic systems, there is a group of robots that seek to achieve a collective task. Applications include environmental monitoring, search and rescue, and programmable self-assembly. Settings can range from a small team of cooperative robots to a swarm of many interacting agents. An essential feature of such systems is that individual robots make decisions based on available local information as well as limited communications with other robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized paradigm, both measurements and decisions are distributed among multiple actors.

The Robots are Coming – to your Farm

Girish Chowdhary, Associate Professor, UIUC

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KAUST

Featured Sessions
For more information please visit the website

The KAUST Research Conference on Robotics and Autonomy 2022

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B19 H1

KAUST Robotics, Intelligent Systems and Control Lab (RISC Lab) will host the KAUST Research Conference on Robotics and Autonomy 2022 (#RobotoKAUST) from February 28 until March 2, 2022. The conference will address the most recent trends of robotics application in a range of disciplines. To attend RobotoKAUST Gala, please, read more about the event and follow the event registration instructions.

Autonomy for recreational vessels: Safe intelligence at your finger tips Pablo Afman, ATL Robotics

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B2 L5 R5220

Abstract Thanks to ever improving sensing, computing, and actuating technologies, autonomy is fast becoming a practical tool for many marine applications. Pushed by the general interest in autonomy and attracted by its underlying capabilities, this talk will present robotic technologies that enable safety and convenience for recreational boaters and marine professionals alike. Beginning with a Grady-White offshore fishing boat powered by two 425 HP engines, we present demonstrated functionalities such as collision avoidance, auto-docking, dynamic positioning, track and follow, and "take me

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

CEMSE Welcomes new Professor: Jeff Shamma

1 min read ·
The CEMSE Division has recently welcomed a new Professor of Electrical Engineering, Dr. Jeff Shamma. Prof. Shamma joins from the Georgia Institute of Technology (Georgia Tech).

Best Business Plan at VIEW 2016

1 min read ·
Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.

Jeff Shamma elected IFAC fellow 2014-17

1 min read ·
The award is an international distinction given to scientists for their contributions in the fields of interest of IFAC under the form of technical publications, patents, control solutions, products, software, and leadership in research, development, and education.

Team KAUST places 3rd at the Mohamed Bin Zayed International Robotics Challenge

1 min read ·
A team from the King Abdullah University of Science and Technology (KAUST) competed in the inaugural Mohamed Bin Zayed International Robotics Challenge (MBZIRC), held in Abu Dhabi, UAE, March 2017, and took a 3rd place finish in the ground robotics challenge.

KAUST hosts International Graduate School on Control event

1 min read ·
KAUST recently acted as a host campus for the European Embedded Control Institute's International Graduate School on Control (IGSC). The IGSC is an annual series of 27 one-week graduate modules focusing on different topics of networked and embedded control and is taught to eligible attendees at different locations worldwide. The series is co-sponsored by the Institute of Electrical and Electronics Engineers Control Systems Society and the International Federation of Automatic Control.

Professor Shamma awarded International Federation of Automatic Control High Impact Paper Award

1 min read ·
KAUST Professor of Electrical Engineering Jeff Shamma has been awarded The International Federation of Automatic Control Council’s (IFAC) High Impact Paper Award 2020.

KAUST startup tackles COVID using AI and robotics

1 min read ·
The COVID-19 pandemic has activated vital innovation and R&D initiatives to tackle the crippling effects, health ramifications, and overall impact of the coronavirus on global societies and economies. In recent months, the King Abdulaziz City for Science and Technology (KACST), based in Riyadh, Saudi Arabia, launched a COVID-19-themed virtual accelerator aimed at supporting talented innovators, aspiring entrepreneurs, and innovative small companies dedicated to finding solutions to mitigate the spread of the virus through protection, prevention, tracking/detection, and treatment solutions.

KAUST RISC lab hosts Center of Excellence for Aeronautics and Astronautics(KACST)

1 min read ·
KAUST Robotics, Intelligent Systems, and Control (RISC) Lab hosts a 3-day visit by personnel from the Center of Excellence for Aeronautics and Astronautics at King Abdulaziz City of Science and Technology ( https://ceaa.kacst.edu.sa/). During their visit, a team of ten, led by Dr. Hesham Shageer of KACST attended talks by RISC Profs. Eric Feron and Shinkyu Park on diverse aspects of robotics. The KACST team also visited FalconViz ( http://www.falconviz.com/) and Firnas ( https://www.firnas.aero/home.php)Two KAUST startups relying on drone technology to perform a variety of tasks, such as

The KAUST Research Conference on Robotics and Autonomy 2022 #RobotoKAUST

1 min read ·
Building upon our successful online conference in spring 2021, the KAUST Robotics, Intelligent Systems, and Control Lab (RISC Lab) will host the KAUST Research Conference on Robotics and Autonomy 2022 (#RobotoKAUST2022) from February 28 until March 2, 2022. The conference will address the most recent trends of robotics application in a range of disciplines.

Learning about robots and coding through play

1 min read ·
"Hour of Code,” a worldwide initiative with the scope to teach children the basics of computer science, recently took place at The KAUST School Kindergarten. During Computer Science Education Week, December 5-9, 2021, researchers from the KAUST RISC Lab volunteered to help the children to “demystify” code and grasp their first concepts of robotics.

Professor Park receives O. Hugo Schuck Best Paper Award at the 2022 American Control Conference

1 min read ·
Professor Shinkyu Park has been awarded the 2022 O. Hugo Schuck Best Paper Award (Theory) from the American Automatic Control Council (AACC) for his paper investigating a new mathematical framework to study decision making in multi-agent systems.

Usman Amin Fiaz

Areas of Expertise and Research Interests Robotics Intelligent Systems Design Multi-Agent Control Systems Aerial Grasping Self-Assembly & Self-Reconfigurable Systems ​ Education Profile PhD in Electrical Engineering, University of Maryland, College Park (UMD), United States of America (current) MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) BS in Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan (2015)

Tarek H. Mahmoud

Areas of Expertise and Research Interests ​Computer Vision Machine Learning Robotics Education Profile ​MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) ​​BS in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia (2016)

Shinkyu Park

Professor Park advances robotic science by building on individual robots’ core sensing, actuation and communication capabilities in distributed information processing, decision-making and manipulation.

Silvio Giancola

Silvio Giancola is a Research Scientist at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL), part of the Center of Excellence for Generative AI (GenAI).

Sarah A. Toonsi

Areas of Expertise and Research Interests Robotics Control Systems Programming Education Profile ​BS in Electrical and Computer Engineering, Effat University, Saudi Arabia (2017) ​

Samet Guler

Postdoctoral Research Fellow, Electrical and Computer Engineering
Samet Güler (M’16) received the B.S. degree in electrical and electronics engineering from Eskisehir Osmangazi University, Turkey, in 2010, the M.S. degree in electrical and electronics engineering from Bilkent University, Turkey, in 2012, and the Ph.D. degree in mechanical and mechatronics engineering from University of Waterloo, Waterloo, ON, Canada, in 2015. He worked as a short term Researcher at the University of Waterloo in 2016. He is currently a Postdoctoral Fellow at the Robotics, Intelligent Systems and Control Lab in King Abdullah University of Science and Technology, Kingdom of

Ryan Maxwell

Rui Li

Rui Li is an Electrical Engineering Ph.D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Rui Li obtained bachelor of engineering degree in Telecommunication Engineering from Xidian University in China in 2013. After that in 2016, he received his master degree in Electrical Engineering from University of Science and Technology of China in 2016. Rui started his career life as a Research Assistant at State

Renzo Caballero

Education 2022.05.24 Ph.D. Research Proposal: "Theory and Dynamics of Self-Repair Systems" Committee Members: Prof. Eric Feron, Prof. Shinkyu Park, Prof. Diogo Gomez, Prof. David Keyes, Prof. Taous-Meriem Laleg 2019.06 - 2023.12 Ph.D. in Applied Mathematics and Computational Sciences, KAUST, KSA 2017.08 - 2019.06 M.Sc. in Applied Mathematics and Computational Sciences, KAUST, KSA ​2010.02 - 2016.06 B.Sc. in Electrical Engineering, Universidad de la Republica, Montevideo, Uruguay Professional Profile 2021.08 - 2021.12 Teaching Assistant, CEMSE, KAUST, KSA 2017.01 - 2017.04 Models Analyst, UTE

Qilin Sun

Qilin Sun is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qilin Sun obtained his bachelor degree in Optical Information Science and Technology from Huazhong University of Science and Technology in Wuhan, China in 2015. After that, he joined KAUST and received his master degree in Electrical Engineering in 2017.Qilin is a CEO&Founder at Point Spread Technology, China. Research Interest Qilin is interested in Computational

Noureddine Toumi

Areas of Expertise and Research Interests Robotics Control Multi-agent systems Cooperative aerial transportation Education Profile BS ​Engineering from Tunisia Polytechnic School, Major: signal processing,systems conception and control.

Obadah Wali

Education Profile 2021.06 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2019.09 - 2021.05 M.S. in Mechanical Engineering, KAUST, KSA 2011.09 - 2016.05 B.S. in Mechanical Engineering, King Fahd University of Petroleum and Minerals, KSA Work Experience 2016.08 - 2019.08 Mechanical Engineer, SABIC Terminal Services (SABTANK), KSA Selected Publications O. Wali, M.T. Shahab, E. Feron, “A Non-planar Assembly of Modular Tetrahedral-shaped Aerial Robots”, 2023 IEEE International Conference on Robotics and Automation (ICRA). (Accepted). Areas of Expertise and Research Interests Drones

Nojood Almayouf

Nojood Almayouf is a Graduate Research Assistant in the Distributed Systems and Autonomy (DSA) Lab within the Robotics, Intelligent Systems and Control (RISC) Research Initiative. Education M.S. in Electrical Engineering, KAUST, Saudi Arabia (Aug 2017 - Dec 2019). Visiting graduate student, Stanford University, United States (Jun 2017 - Aug 2017). B.S. (with first honor) in Electrical and Computer Engineering, Effat University, Saudi Arabia (Jan 2013 - Jun 2017). Professional Profile Graduate Teaching Assistant for ECE 271A/ME 221A: Linear Control Systems Course, KAUST (Aug 2022 - Dec 2022)

Norah Alghamdi

Education 2021.07 - Present Ph.D. Student in Electrical and Computer Engineering, KAUST, KSA 2018.08 - 2021.06 M.S. in Electrical and Computer Engineering, KAUST, KSA 2013.08 - 2018.07 B.S. in Electrical and Computer Engineering, King Abdulaziz University, KSA Selected Publications N.K. Alghamdi, D.A. Shamma, J.S. Shamma, "Analyzing the Passivity of the Human Decision Making Rule in a Congestion Game Experiment" 2022 IEEE American Control Conference (ACC), 4826-4831, (2022). Areas of Expertise and Research Interests ​Game Theory Multi-robot Systems Robotics Human-robot Interaction

Nawaf Alotaibi

Education 2020.08 - 2022.05 M.S. Student in Mechanical Engineering, KAUST, KSA 2016.08 - 2020.05 B.S. in Mechanical Engineering, Georgia Institute of Technology, USA Professional Profile 2019.06 - 2019.08 Research Intern, RISC Lab, KAUST, KSA Areas of Expertise and Research Interests ​Robotics Autonomous Vehicles Machine learning Vision

Mutaz Aljafari

Education 2021.05 - 2021.08 Visiting Student, The CEMSE Saudi Summer Internship (SSI), King Abdullah University of Science and Technology, KSA Project: "Air-Air Drone Detection Using Modular Radar" 2015.09 - 2021.12 B.S in Aeronautical Engineering, KAUST, KSA Professional Profile 2021.05 - 2021.08 Research Intern, RISC Lab, KAUST, KSA 2020.01 - 2020.12 Aerospace Engineer, R&D, SarSat Arabia, KSA 2018.06 - 2018.07 Research Intern, Automotive Department, Nagoya University, Japan 2018.01 - 2020.12 Founder - Leader, Faculty of Engineering Manufacturing Club, KSA 2017.06 - 2017.08 Assistant

Mohammed M. Alrashed

Mohammed M. Alrashed research interests are in ​Systems and Control with robotic applications and Mathematical Theory of Rational Behavior. Master Thesis Abstract The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. The lack of understanding of panic stampedes still causes hundreds of fatalities each year, not to mention the scarce methodical studies of panic behavior capable of envisaging such crowd dynamics. Under those circumstances, there are thousands of

Mohammed Abdullah Algarni

Areas of Expertise and Research Interests Unmanned Aerial Vehicle (UAV) Robotics Autonomy Aerial Landing Education Profile MS in Mechanical Engineering, King Abdullah University for Science and Technology (KAUST), Saudi Arabia (2014​​) ​BS in Mechanical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia (2009)

Mohamad T. Shahab

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education 2020, PhD in Electrical and Computer Engineering, University of Waterloo 2015, MSc in Systems Engineering, KFUPM 2007, BSc in Control & Instrumentation Systems Engineering, KFUPM Professional Profile 2020 - Present, Postdoctoral Fellow, RISC Lab, KAUST 2016 - 2020, Research/Teaching Assistant, University of Waterloo 2012 - 2015, Research Engineer, Baker Hughes 2007 - 2011, Research/Teaching Assistant, KFUPM Selected Publications O. Wali, M.T. Shahab, E. Feron, "A Non-planar Assembly of Modular Tetrahedral-shaped Aerial Robots", 2023 IEEE International Conference on Robotics and

Mohamed A. Mabrok

Postdoctoral Research Fellow, Electrical and Computer Engineering
From 2015 till 2017 Mohamed A. Mabrok was a research associate at Robotics, Intelligent Systems & Control (RISC) research group at King Abdullah University of Science and Technology (KAUST). Currently, he is an Assistant Professor at the Faculty of Engineering at the Australian College of Kuwait. He obtained his Ph.D. in the field of systems and control in 2013 from the University of New South Wales at Australian Defence Force Academy, Canberra, Australia. Mohamed also holds a bachelor degree in applied mathematics and a master degree in applied mathematics/quantum physics. Mohamed's research

Matthias Mueller

Matthias Mueller was an Electrical and Computer Engineering Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at Visual Computing Center (VCC) under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Matthias graduated with a B.Sc. in Electrical Engineering and Math Minor from Texas A&M University in 2011. After graduation, he joined P+Z Engineering in Munich, Germany as an Electrical Engineer and worked for 3 years on the development of mild-hybrid electric machines at BMW. In 2014, he started his M

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Kuat Telegenov

An experienced Robotics Research Engineer and Laboratory Manager.

Klemens Katterbauer

Klemens Katterbauer is a AI specialist and software engineer working on robotics and artificial intelligence software development at EXPECARC in Saudi Aramco. His focus is on bridging the gap between sustainable energy transition development and the power of artificial intelligence. Klemens has published more than 50+ journal and conference publications, and has more than 20+ granted and submitted patents. Before this he was the IT Infrastructure Manager and Business Process Consultant for Eastern Europe, Middle East & Asia Pacific Region at the XXXLGroup (2nd largest furniture retailer in the

Khalid Alzuhair

M.S. Degree, Electrical and Computer Engineering, KAUST (King Abdullah University of Science and Technology).

Jun Chen

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Jun Chen received his Ph.D. degree in mechanical engineering from Temple University, USA in 2021, and his M.S. degree in electrical and computer engineering from Stevens Institute of Technology, USA in 2017. Before that, he received his B.S. degree from Hefei University of Technology, China in 2015. His research interests lie in robotics, distributed multi-robot systems, robot learning, etc.

Juman AlAbdullatif

Education Profile 2020.08 - 2022.05 M.S. Student in Electrical and Computer Engineering, KAUST, KSA 2016.08 - 2020.05 B.S. in ECE, Rose-Hulman Institute of Technology, USA Areas of Expertise and Research Interests ​Robotics Control HRI

Jiming Jiang

Areas of Expertise and Research Interests Circuit and System, Robotics, Wireless Sensor Network Education Profile PhD in Electronics, Cambridge University, United Kingdom (2010) MS in Analog and Digital Integrated Circuit Design, Imperial College, United Kingdom (2004​) BS in Electronic Engineering, Beijing University of Technology, China (2003)​

Hussein Hussein

Postdoctoral Research Fellow, Electrical and Computer Engineering
Hussein Hussein is a Postdoctoral Fellow at the Integrated Circuits and Systems Research Group under the supervision of Professor Hossein Fariborzi in King Abdullah University of Science and Technology (KAUST). Hussein obtained his PhD in engineering sciences and microsystems from University of Franche-Comté. He has a mechanical engineering degree from the Lebanese University and a Master degree in Mechatronic systems from University of Compiegne . Research Interests Mechanics and Mechatronics at macro and micro scale. Modeling, design and fabrication of microsystems and MEMS structures

Haya Alsharif

Areas of Expertise and Research Interests ​​Control Systems Computer Vision Machine Learning Robotics Education Profile ​​BS in Electrical and Computer Engineering, King Abdulaziz University (KAU), Saudi Arabia (2016)

Hassan Jaleel

Postdoctoral Research Fellow, Electrical and Computer Engineering
Areas of Expertise and Research Interests ​Distributed control of multi agent systems using game theory and algebraic graph theory Energy aware algorithms for multi agent systems Cyber physical systems Stochastic geometry Coverage problems for sensor networks Education Profile PhD in Electrical & Computer Engineering, Georgia Tech (2013) MS in Electrical & Computer Engineering, Georgia Tech (2011) BSc in Electrical Engineering, UET (2007)

Hassan S. Abdelraouf

Ph.D. Student, Mechanical Engineering
Education 2018.09 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2015.01- 2017.01 M.S. in Aerospace Engineering, Cairo University, Egypt 2010.09 - 2014.05 B.S. in Aerospace Engineering, Cairo University, Egypt Professional Profile 2021.01 - 2021.05 Teaching Assistant, ECE, KAUST, KSA 2020.08 - 2020.12 Teaching Assistant, ECE 376, ECE, KAUST, KSA 2020.01 - 2020.05 Teaching Assistant, ECE 372, ECE, KAUST, KSA Honors and Awards 2019.12 "Best Aerial Team" award in the European Robotics League team competition Areas of Expertise and Research Interests Control Theory Aerospace systems

Gilberto Mendoza Chavez

Areas of Expertise and Research Interests Robotics Control Systems Distributed Systems Education Profile Masters in Electrical Engineering, King Abdullah University of Science & Technology, KSA (2016)​ ​Bachelors in Mechatronics Engineering, Monterrey Institute of Technology and Higher Education, Mexico (2014)

Frankly Toro

Frankly Toro is a Ms/Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career He is a graduate of National University of Colombia where he received a B.S. degree in Mechatronics. Education Profile B.S., Mechatronics , National University of Colombia, 2020

Fatema Alhani

Education 2020.08 - Present M.S. D. Student in Electrical Engineering, King Abdullah University of Science and Technology, KSA 2016.07 - 2020.05 B.S. in Electrical and Computer Engineering, North Carolina State University, USA

Fat-hy Omar Rajab

Master's Thesis Abstract The multi-agent robotic system has been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased efficiency in multi-agent systems is achieved by the execution of the task in a cooperative manner. But to achieve cooperation in multi-agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent systems rely on the use of the GPS to provide global positioning information which suffers great deterioration in performance in indoor applications, and also

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Doha Raik Hamza Mohamed

Introduction ​Doha Raik Hamza Mohamed remained at KAUST as Associate Research Scientist under the supervision of Prof. Je Shamma after graduation of Ph.D. from the CTL group at KAUST in 2014. Education and early career Doha Raik obtained her B.S. in Communications and Electronics Engineering, Alexandria University, Alexandria, Egypt in 2001. Then in 2008, she went to Nile University, Cairo, Egypt to get M.S. in Wireless Communications. After graduation in 2010, she earned a Ph.D. degree in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal. Research

Brian Parrott

Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, King Abdullah University of Science and Technology, KSA 2017.06 - 2019.06 MBA, HKUST, Hong Kong-China ​2010.08 - 2011.12 MS in Chemical and Biological Engineering, KAUST, KSA 2006.09 - 2010.06 BS in Biology, University of Washington, USA Professional Profile 2012.05 - Present Research Scientist, Research and Development Center, Saudi Aramco, KSA Honors and Awards 2014.01 CEO Excellence Award 2013.01 International Glory Medal, International Federation of Inventors' Associations Selected Publications B. Parrott, P. Carrasco

Bacem Ben Nasser

Bacem Ben Nasser is a postdoctoral researcher fellow in the Electrical Engineering department within the CEMSE division. He obtained his Ph.D. degree in Sciences and Technology-Mathematics from the University of Sfax, Faculty of Sciences of Sfax, Tunisia, where he worked in Lyapunov and control theories, nonlinear integral inequalities and time scale calculus. In January 2017, he was affiliated with the University of Kairouan, Tunisia, at the Higher Institute of Applied Sciences and Technology of Kairouan as assistant professor of applied mathematics. Dr. Ben Nasser joined the research group

Aslihan Kartci

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education and Early Career Dr. Aslihan Kartci received her Ph.D. degree in Electronics from the Brno University of Technology, the Czech Republic in 2019 and M.S. degree from Yildiz Technical University, Turkey, in 2015. She was with the Research and Development Center of Komax Wire, Turkey from 2012 to 2014, engaged in software development of production planning systems, server management, project management for functional test machines. She was awarded with “Travel Grant” from IEEE flagship conference on Circuits and Systems, namely ISCAS 2015. She was „Congress Publicity Chair” of the

Arman Ibrayeva

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student, KAUST, KSA 2018.08 - 2020.07 M.S. in Mechanics, Kazakh National University, Kazakhstan 2014.08 - 2018.07 B.S. in Mechanics, Kazakh National University, Kazakhstan Professional Profile 2020.03 - 2021.11 Researcher, Institute of Mechanics and Machine Sciences, National Academy of Sciences, Kazakhstan 2019.06 - 2021.11 Engineer, KOLSAJ STROJ, Kazakhstan 2014.05 - 2015.05 Laboratory assistant, Fluid and Gas Mechanics Lab, Kazakh National University, Kazakhstan Selected Publications S. Ibrayev, N. Jamalov, A. Ibrayeva, G. Mukhambetkaliyeva "Optimal

Angelica Coronado Preciado

Anwaar Alghamdi

‌ Education: B.Sc., Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia, 2017 Research Interests: Control systems Autonomous robotics Wireless sensor networks

Anton Kim

I am working on object manipulation pipeline for robotic assembly which can be used for manufacturing and assembly lines.

Amjad Felemban

Areas of Expertise and Research Interests ​Control Systems Robotics Multi-Agent Systems Machine Learning Education Profile ​BS in Electrical Engineering, Purdue University, USA​ (2016)

Amin Almozel

Education 2021.01 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA 2018.08 - 2019.12 M.S. in Electrical and Computer Engineering, KAUST, KSA 2014.08 - 2018.05 B.S. in Electrical and Computer Engineering, University of Rochester, USA Selected Publications A. Almozel, J.S. Shamma, "Competitive Drone Racing Using Asymmetric Games" IEEE 18th International Conference on Ubiquitous Robots (UR), 349-356, 8 (2021). DOI: 10.1109/UR52253.2021.9494629 Areas of Expertise and Research Interests Autonomous and Intelligent Systems​

Alfiya Kulmukhanova

Areas of Expertise and Research Interests Multi-agent systems Smart Grids Game theory applications for Smart Grids Education Profile Bachelors in Electrical and Electronic Engineering, Nazarbayev University, Kazakhstan (2016)
<> optimization (66)
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Everything you always wanted to know about single machine large-scale graph analytics

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B9 L2 H1

Abstract: Graphs are a natural way to capture relations among objects or people. This has led to graph processing systems being used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. The focus of our recent work is on single machine graph processing systems where the graph is processed from main memory or external storage. We perform a systematic comparison of various techniques proposed to speed up in-memory multicore graph processing. In addition, we take an end- to-end view of execution time

Estimating the expected information gain in Bayesian optimal experimental design

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B9 L2 H1

Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Spectrum Management for Future Generations of Cellular Networks

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B1 L2 R2202

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

Virtual sensing: methods and applications

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B9 L2 H1 R2322

Abstract Due to the inherent complexity, physical or economical constraints, a number of important physical and biological systems are inaccessible. Only few, often indirect, experimental data are available to extract information or to manipulate and control the system. Introduced as a promising technology to respond to the growing need for reliable, accurate, fast, robust and low-cost sensing strategies, virtual sensors are gaining importance in several fields. These sensors combine the available data with effective estimation methods and algorithms to access non-measurable variables of

Use Mathematics and get a SPARQL Query Engine (almost) for Free! It’s MAGiQ!

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B9 L2 H1 R2322

Existing RDF engines are designed for specific hardware architectures; porting to a different architecture (e.g., GPUs) entails enormous implementation effort. We explore sparse matrix algebra as an alternative for designing a portable, scalable and efficient RDF engine.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Space-Time Tomographic Reconstruction of Deforming Objects

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B1 L2 R2202

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

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KAUST

Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

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KAUST

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

Stochastic Optimization in Target Positioning and Location-based Applications

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KAUST

This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

Powering the bottom line

1 min read ·
A risk-based optimization scheme boosts confidence and profitability for future mixed-technology power plants.

Aerial Path Planning for Urban Scene Reconstruction: A Continuous Optimization Method and Benchmark

1 min read ·
Small unmanned aerial vehicles (UAVs) are ideal capturing devices for high-resolution urban 3D reconstructions using multi-view stereo. Nevertheless, practical considerations such as safety usually mean that access to the scan target is often only available for a short amount of time, especially in urban environments. It, therefore, becomes crucial to perform both view and path planning to minimize flight time while ensuring complete and accurate reconstructions. In this work, we address the challenge of automatic view and path planning for UAV-based aerial imaging with the goal of urban

Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"

1 min read ·
​Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"​. It is a 45 minutes talk scheduled on the 6th of July 2019.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

An Airline observatory created by Passengers for Passengers

1 min read ·
The COVID-19 pandemic has generated a significant worldwide disruption to flight operations, affecting the lives of millions of passengers.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

Yongliang Yang

Research Scientist, Computer Science
Yongliang Yang was a Research Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in Department of Computer Science at University of Bath in United Kingdom. Education and Early Career Yongliang received his Bachelor degree in Computer Science and Technology in 2004, from Tsinghua University. He continued to pursue his Master and Ph.D. degree in Graphics and Geometric Computing Group in Tsinghua, under the supervision of Prof. Shi-Min Hu. In 2009, he joined KAUST as a Postdoctoral Fellow working with Prof

Tariq Alturkestani

Ph.D. Student, Computer Science
Tariq is a PhD student in Computer Science working under the supervision of Professor David E. Keyes. He is working on developing a library that enables users to overlap I/O and compute. Tariq holds a Bachelors degree from Penn State University and a Master from KAUST. Education Profile MS, Computer Science, King Abdullah University of Science and Technology (KAUST), 2014. BS, Computer Science, Penn State University, USA, 2013.

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Ricardo Manuel Pinto de Lima

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.

Rafal Szlendak

Rafał Szlendak is an MS/PhD student in the AMCS program under the supervision of Prof. Peter Richtarik. His biggest passions are research, weightlifting and travelling. His career path is motivated by his endless passion for self-development and discovery. Education and Early Career Rafał Szlendak has obtained his Bachelor's degree in Mathematics and Statistics from University of Warwick Research Interest In his research Rafał focuses on stochastic gradient descent algorithms for large-scale machine learning problems. His primary goal is developing state-of-the-art methods with sound

Ramzi Idoughi

Research Scientist, Computer Science
Ramzi Idoughi is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ramzi obtained his engineering degree in Space System and Image Processing from National Higher French Institute of Aeronautics and Space (ISAE/SUPAERO) in France in 2011. Later, he received his master degree in Signal, Image, Acoustics and Optimization (SIAO) from Higher Educational Institution in Toulouse (ENSEEIHT) in France in 2011. After that in 2015, he received his doctoral

Qizhou Wang

Qizhou Wang is an M.S./Ph.D. candidate in the KAUST Primalight research group under the supervision of Professor Andrea Fratalocchi. Before joining KAUST, Qizhou obtained a bachelor's degree in communication engineering from the University of Electronic Science and Technology, China. Research Interests Qizhou's research interests include machine learning, optimization theories and computer vision.

Qilin Sun

Qilin Sun is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qilin Sun obtained his bachelor degree in Optical Information Science and Technology from Huazhong University of Science and Technology in Wuhan, China in 2015. After that, he joined KAUST and received his master degree in Electrical Engineering in 2017.Qilin is a CEO&Founder at Point Spread Technology, China. Research Interest Qilin is interested in Computational

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Mher Safaryan

Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Laurent Condat

I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Krishnendu S Tharakan

Visiting Student (former), Electrical and Computer Engineering
Visiting student at King Abdullah University of Science and Technology (KAUST).

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Itsikiantsoa Randrianantenaina

Itsikiantsoa Randrianantenaina received the Engineering Diploma degree from the Institut National des Postes et Telecommunications, Rabat, Morocco, in 2013, and the M.S. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, from June 2015- June 2019, where she earned the Ph.D. degree and Joined NEOM, Saudi Arabia, as a Research Engineer after graduation. Her research interests include full-duplex and NOMA systems, interference management, FogRAN architecture, stochastic geometry, and optimization. Education Profile Ph.D

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Guangming Zang

Research Engineer, Computer Science
Guangming Zang is a research engineer in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Guangming obtained his bachelor degree in Software Engineering from China University of Geosciences in Wuhan, China in 2013. He received his master degree in Computer Science from University of Chinese Academy of Sciences in China. Before joining KAUST, Guangming was a research assistant (2013 – 2014) at The National Space Science Center at Chinese Academy of Sciences

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Francesco Orabona

Associate Professor, Computer Science
Achieving "parameter-free" machine learning is the primary focus of Professor Francesco Orabona. He is particularly interested in designing superior algorithms to train deep learning models.

El Houcine Bergou

Postdoctoral Research Fellow, ‌InfoCloud
As of January 2019, El Houcine Bergou is a Postdoctoral Research Fellow at Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to this, he spent a year as a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Dr. Bergou has a PhD degree in numerical methods for least squares problems with application to data assimilation from the National Institute of Posts and Telecommunications (INPT) in

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Ebrahim Al-Safadi

Visiting B.S. Student, Information Science Lab
Internship at King Abdullah University of Science and Technology (KAUST).

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

Biao Zhang

Ph.D. Student, Computer Science
Biao Zhang is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. Education and Early Career Biao Zhang obtained his bachelor degree in Mathematics and master degree in Applied Mathematics from Xi’an Jiaotong University in China. Research Interest Biao Zhang is mainly interested in machine learning and computer vision. Education Profile M.S., Applied Mathematics, Xi’an Jiaotong University, China B.S., Mathematics, Xi’an Jiaotong University

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Alyazeed Basyoni

M.S. Student, Computer Science
Alyazeed Basyoni is a CS Master Student under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alyazeed joined Carnegie Mellon University in Pittsburgh, Pennsylvania and earned his bachelor degree in Computer Science with minor in Mathematics in 2018. Education Profile B.S., Computer Science, Carnegie Mellon University, USA, 2018

Ali H. AlNasser

M.S. Student, Computer Science
Ali AlNasser is a Masters student in Computer Science. His research interests are scaling deep learning algorithms available now to be used on multicore computers by leveraging the high parallelism in Deep learning. Why KAUST? The abundance of opportunities available in KAUST. Education Profile B.S, Electrical & Computer Engineering, University of Colorado at Boulder. B.S, Applied Mathematics, University of Colorado at Boulder.

Alexandra Aguiar Gomes

Alexandra Aguiar Gomes has been a university educator since 1996. Her area of expertise is optimization, with current interests in the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. Alexandra finds joy in simplifying mathematical concepts, helping her students discover the practicality of mathematics in their fields.

Ahmed Douik

Visiting B.S. Student, Computer, Electrical and Mathematical Sciences and Engineering , Quantitative Researcher at Chicago Trading Company PhD candidate at California Institute of Technology

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is
<> IoT (63)

Advancing Security Red-Teaming in Cyber-Physical Systems via AI-Driven Side-Channel Analysis

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B9 L3 R3223

This talk will systematically reveal security and privacy vulnerabilities in cyber-physical systems by characterizing contactless side-channel attacks on various components and proposing effective defense methods using hardware-software co-design and AI-driven techniques, while also outlining future research directions for developing secure and privacy-preserving CPS platforms.
KAUST-CEMSE-ECE-Omar-Khalifa-Winner-Of-Non-Terrestrial-Networks-for-6G-(NTNYS'24

KAUST ECE students achieve high honors in Competition on Non-Terrestrial Networks for 6G (NTNYS'24)

4 min read ·
KAUST Ph.D. candidate Omar Khalifa earned an impressive second place in the global Competition on Non-Terrestrial Networks for 6G (NTNYS'24). The IEEE Future Networks Initiative organized the competition as part of the "Connecting the World from the Skies Global Forum," which took place last November in Riyadh. Second place, and a prize of $10,000, was awarded to Khalifa based on his work titled "Supplying Data for Digital Twins in Green Smart Cities: A Non-Invasive NTN-IoT Approach." The international forum, which focused on the theme “Shaping the Future of Converged Connectivity,” was hosted
Sustainable solutions in water conservation

KAUST-Engineered tool drives sustainable solutions in water conservation

3 min read ·
KAUST’s startup EcoStream cuts water waste by 50% while promoting affordability and sustainability, winning the 2024 StartSmart prize.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

A voice to the voiceless: Montserrat Ramirez De Angel takes “Best Pitch” award at IEEE 2024 Sensors Conference

3 min read ·
Montserrat Ramirez de Angel wins the Best Pitch Award at IEEE 2024 Sensors Conference for her innovative work in biosensors and sensor technology.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Advanced MTJ Sensory Devices for Industrial and Healthcare Applications

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B3 L5 R5209

Magnetic sensors are deployed in many applications such as automotive, consumer electronics, navigation and data storage devices. Their market’s growth is driven by demands of higher performance and more integration; primarily to assist in the advancement of Internet of Things (IoT) and smart systems.

5G IoT Networks: from Massive access to URLLC

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B1 L3 R3119

Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

Physical Layer Security of Wireless-powered NOMA-based Cognitive Radio Networks: Fundamentals & Challenges

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B1 L2 R2202

Abstract In the last decade, with the emergence of the internet of things (IoT) as well as machine-to-machine (M2M) paradigms, future wireless networks are targeting new critical services such as remote healthcare, surveillance, transportation, etc. In this regard, with the massive expected number of connected IoT and M2M devices, several requirements have been imposed by the regulatory bodies onto the deployment of 5G network. These requirements include, but not limited to, higher connectivity, reduced latency, as well as increased spectral and energy efficiency. Interestingly, the

Integration Strategy for Standalone Compliant Interactive Systems for Add-on Electronics

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B9 L3 R3125

Physically compliant electronics are scientifically intriguing, mechanically complex, ‎technologically ‎challenging but with huge socio-economical potential. Until now the target applications ‎for ‎flexible electronics have been limited to displays, solar cells, printed batteries, wearables, and ‎implantable. However, with the emergence and growth of Internet of Things (IoT) devices ‎worldwide ‎from nearly 27 billion in 2017 to 125 billion in 2030, in this PhD research, expanding the ‎horizon of ‎applications for flexible electronics toward existing “things” will be explored. Low-cost sustainable materials as active electronic materials and a ‎Do-It-Yourself (DIY) integration strategy is used to build “Add-on” standalone sensory system which can be ‎attached to any ‎existing things like a decal. Such electronics can also be embedded in newly minted ‎devices specially ‎using additive methods.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Nanomaterials and Nanostructure Based Energy Efficient Edge and Fluidic Computing for Internet of Things Applications

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B9 L2 H1

In this talk, I will address these questions with a focus on smart and multifunctional nanomaterial-based memory devices than can sense different physical qualities of the environment (Memsors) in addition to pressure-driven microfluidic logic gates which can process and analyze non-electronic media in an attempt to explore, enable and empower a wider range of IoT applications.

Paving the Way for Next Generation Wireless Networks

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B9 L2 H1 R2322

In this talk, I will introduce several ongoing projects at the networking lab in KAUST. I will start by highlighting our research profile along with our mission. Then, I will walk you through our projects and contributions in the domains of the internet of things, visible light communication, underwater communication, and future 6G networks. I will focus on the challenges facing each project, highlight our solution methodology, and discuss some performance evaluation results. I will focus on our work on Aqua-Fi, which aims at bringing the Internet into the underwater environment. I will also focus on our recent project on the communication via breath.

Sensing, Localization, and Communications to Enable Future IoT Systems

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KAUST

In this talk, Tareq will present his research contributions and future directions to advance some critical IoT-enabling technologies: sensing, localization, and communications. He will demonstrate how we take advantage of structure in sensed-data and sensor arrays. This structure can help mitigate sensing uncertainties, improve localization accuracy, and enhance the performance of communication systems, all while reducing the computational overhead. The Internet of Things (IoT) has ushered a new era in many fields including retail, medicine, agriculture, and the automotive industry. In fact, it is projected that by 2025, one trillion IoT devices will be deployed worldwide: the equivalent of 1000 devices per person. To reach such a scale, major advancements are needed in various IoT-enabling technologies.

IoT via 5G and Beyond 5G Satellite Systems

Prof. Giovanni Giambene, Information Engineering and Mathematics, University of Siena, Italy

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KAUST

One of the key drivers for next-generation mobile communications is the support of the Internet of Things (IoT), with billions of objects connected to the Internet and very low latency. The 5G technology will support the realization of smart cities, smart environments, and big data applications.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Scheduling tasks in energy harvesting IoT

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B4 L5 R5209

Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.

Towards Transparency of IoT Device Presence

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B9 L2 H2 H2

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.

Flash Microfluidics, rapid Lab on a Chip fabrication method for cost- effective in situ production.

1 min read ·
From DNA sequencing to environment monitoring, microfluidics devices have been broadly employed, but their production is more and more time-consuming and expensive. Researchers from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, have developed an innovative prototyping method for Lab on a Chip (LOC) production to plummet costs while augmenting performances.

Plant Wearables and Airdropped Sensors Could Sow Big Data Seeds

1 min read ·
Stretchable plant wearables and smart tags dropped by drones aim to help give farming a big data makeover. The relatively cheap technologies for mass monitoring of individual plants across large greenhouses or crop fields could get field tests in three countries starting in 2019. The idea came from researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia with expertise in flexible electronics. After talking with colleagues who were cultivating genetically engineered plants in greenhouses, they recognized the need for inexpensive sensors that could be deployed

3D-printed cube dials into energy harvesting

1 min read ·
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.

ISL Student Osama Bushnaq does an Delft Internship

1 min read ·

IoT

Summary of Delft internship : Advanced sensor networks are needed in order to meet the increasing needs of IoT applications, such as automated surveillance, environmental monitoring, smart cities, and so on. Optimal sensor placement, i.e., to select the best subset of sensing locations out of a large set of available locations, keeping in mind the network infrastructure and the inference task, forms an important sensor network design task. In this internship, the offline sensor selection for source estimation is improved by considering different practical issues such as observation accuracy

Single-electrode material streamlines functions into a tiny chip

1 min read ·
Ruthenium oxide is used to integrate energy-storing microsupercapacitors and thin-film electronics at the transistor level.

ISL PhD Student Osama Bushnaq does Internship at UBC

1 min read ·

UAV IoT

ISL PhD Student Osama Bushnaq did an internship at the University of British Coloumbia in Canada. His internship was focused on UAV-IoT. Every year, an average of 2.5 million hectares (ha) is burnt only in Canadian forests, which costs around 500 million to 1 billion Canadian dollars per year. The current forest fire detection methods such as satellite imaging and infrared cameras are not reliable. In order to detect forest fires before getting out of control, a new fire detection method based on Unmanned Aerial Vehicle Internet of Things (UAV-IoT) network was studied during this internship. A

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

The power of light for internet of underwater things

1 min read ·
Light can simultaneously transfer energy and data to underwater devices, but there’s a long way to go before these systems can be deployed.

Nazek El-Atab chosen to attend the 70th Lindau Nobel Laureate Meeting

1 min read ·
Nazek El-Atab, a postdoctoral research fellow, based in the KAUST MMH Labs has been chosen to attend the 70th Lindau Nobel Laureate Meeting. After completing a thorough multi-stage application and selection process, El-Atab was selected to participate in the event scheduled to take place from June 28 to July 3, in Lindau, Germany.

KAUST Prospective Student: Siyuan Chen

1 min read ·
Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Meet KAUST Prospective Student: Asaad AlGhamdi

1 min read ·
Asaad AlGhamdi is a 24-year-old graduate who will join KAUST in the fall from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Asaad will join the University as a M.S. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

To Boldly Go Where No Sensor Has Gone Before: The Movement to Place IoT in Radical New Spaces

1 min read ·
The rapid growth of the world’s population demands more natural resources, food, and space. World renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. The framework of X-IoT consists of three major categories as shown in Figure 1. The first one is the

KAUST alumni receive SPE 2020 Regional Awards

1 min read ·
KAUST alumni Dr. Chinthaka Gooneratne and Dr. Bodong Li both recently received Society of Petroleum Engineers (SPE) 2020 Regional Awards for the Middle East and North Africa Region. Gooneratne won the Regional Drilling Engineering Award, while Li was awarded the Regional Formation Evaluation Award.

KAUST team power ahead with alternative energy research

1 min read ·
With the continued growth in Internet of Things applications, deployments, and devices, highly connected, sustainable smart cities are no longer mere concepts or fantastical future dreams; many are already active and are expanding apace.

Welcoming composite inks into the fold

1 min read ·
A screen-printing approach to creating foldable circuits could make many functional devices easier and cheaper to mass produce.

Nazek El-Atab appointed IEEE Distinguished Lecturer

1 min read ·

IoT

Nazek El-Atab, KAUST assistant professor of electrical and computer engineering and the principal investigator of the Smart, Advanced Memory Devices and Applications (SAMA) Lab, has been recently selected to serve as a Distinguished Lecturer (DL) for the IEEE Electron Devices Society.

Connect the internet of bodies

1 min read ·
A network of body monitoring devices connected using our own tissue could form the basis for the internet of bodies (IoB).

KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon

1 min read ·
A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.

Yousof A. Mashraei

Yousof Mashraei received the M.S. degree from King Abdullah University of Science and Technology (KAUST) in 2014. He joined King Abdulaziz City for Science and Technology and was involved in smart grid and renewable energy since 2012. He joined KAUST as a PhD student in 2014 and specialized in microfabrication and sensors. His research focus on magnetic sensors, integration and hybrid system for health and industrial applications.

Wiem Abderrahim

Postdoctoral Research Fellow, Networking Research Lab
Wiem Abderrahim is a Postdoctoral Fellow under the supervision of Prof. Mohamed Slim Alouini and Prof. Basem Shihada at the Communication Theory Lab, King Abdullah University of Science and Technology (KAUST). Education and Early Career Wiem Abderrahim accomplished her engineering studies in telecommunications with honors at the Higher School of Communications of Tunis, Carthage University in Tunisia in 2013. In 2017, she received her Doctoral Degree in Information and Communications Technologies from the same university. Before joining KAUST, she worked as an adjunct professor at the Higher

Sutanu Ghosh

Sutanu Ghosh is a visiting research scholar under the supervision of Prof. Dr. Mohamed-Slim Alouini at the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) Division in King Abdullah University of Science and Technology (KAUST). Education and Early Career Sutanu Ghosh obtained his Master Degree from Jadavpur University, India and Ph.D Degree from Indian Institute of Engineering Science and Technology, India. Research Interest Sutanu Ghosh is focusing his research in the area of Device-to-Device Communication, optical wireless communication, Energy harvesting, Internet-of

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Raphael Bridi

Ph.D. Student, Electrical and Computer Engineering ,King Abdullah University of Science and Technology (KAUST).

Osama Bushnaq

Ph.D. degree in electrical engineering at the King Abdullah University of Science and Technology (KAUST)

Omar Khalifa

PhD student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST) ,working under the supervision of Prof. Tareq Al-Naffouri.

Nour Kouzayha

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
Research Scientist at King Abdullah University of Science and Technology.

Mohammed Abdullah Hussaini

Senior Embedded System Engineer at KAUST (King Abdullah University of Science and Technology)

Mohammed Ahmed

Postdoctoral Research Fellow (former), Information Science Lab
post-doctoral fellow at L'École de technologie supérieure (ETS) de Montréal (Mtl) where he started on February 2014. he works with Prof. Georges Kaddoum.

Mohamed Saadeldin

Postdoctoral Research Fellow, Sensors
Postdoctoral Research Fellow, Sensors Lab at King Abdullah University of Science and Technology (KAUST), Assistant Professor, University College Dublin .

Mamdouh AlJoud

Intern (former), Information Science Lab
Research Intern at King Abdullah University of Science and Technology (KAUS)

Jikang Deng

Jikang Deng is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jikang Deng obtained his bachelor's degree in Network Engineering from University of Electronic Science and Technology of China (UESTC) in 2023. He was a summer intern in 2022 before joining KAUST. Research Interest Jikang Deng is focusing in the area of wireless communication and Internet of Things (IoT). Education Profile M.S. in King Abdullah University of Science and

Hao Lin

KAUST provides the best environment for us to develop. At KAUST, there are many excellent Professors and friends who can study and do research together. Communication Engineering is an interesting subject that is a key tech for the future. Communication Theory is highly worthy of our in-depth research.

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Ganghui Lin

Ganghui is an MS student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ganghui obtained his bachelor’s degree in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2022. He was a visiting student at CTL (July 2021 – July 2022) before joining KAUST. Research Interest Ganghui is focusing on the area of the next generation of wireless communication, the Internet of Things (IoT), and deep learning. Honors and

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Enas Ahmad

Enas Ahmad obtained her Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Research Interests Enas' research interests included the area of computer networking and systems, specifically software-defined networking, data centers, the internet of things, and participatory sensing. Thesis title: " Energy Efficient Smartphones: Minimizing the Energy Consumption of Smartphone's GPU using DVFS Governors". Education Profile M.sc. in Computer Science, King Abdullah University of Science and Technology

Cian Leo Blake

Cian is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Cian obtained BSc in Physics with a Computer Science minor from New York University Abu Dhabi (NYUAD) in 2023. Alongside his studies, Cian founded an autotech startup called Genius Autos (www.genius-autos.com) that aims to optimize the global used auto parts supply chain in the age of artificial intelligence. Research Interest Cian is focusing in the area of wireless communication

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Asma Abdullah Sarouji

Visiting Student (former), Electrical and Computer Engineering
Research intern at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Tareq Al-Naffouri on health project that has a significant impact on people’s lives.

Asaad S. Alghamdi

Anas Saleh Mohammed

Visiting Student (former), Electrical and Computer Engineering
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> Signal processing (54)

FMCW Radar Applications for Automotive and Biomedical Applications

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B1 L3 R3119

This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon
Sustainable solutions in water conservation

KAUST-Engineered tool drives sustainable solutions in water conservation

3 min read ·
KAUST’s startup EcoStream cuts water waste by 50% while promoting affordability and sustainability, winning the 2024 StartSmart prize.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

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B1 B4 B4214

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

Model Predictive Control Systems for Industrial Applications

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B3 L5 R5209

Model Predictive Control (MPC) is an d advanced control strategy widely used in the process industries and beyond. Therefore, industry is interested in the developments of MPC formulations that can enhance safety, reliability, and economic profitability of chemical processes. Motivated by these considerations, the first part of this talk focuses on the development of methods for integrating process operational safety and process economics within model predictive control system designs.

Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination

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B1 L3 R3119

High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.

CEMSE New Faculty: Ahmed Eltawil, Professor, Electrical Engineering

1 min read ·
Professor of Electrical Engineering, Ahmed Eltawil, recently joined the KAUST CEMSE Division from the University of California (UCI), Irvine, where he had worked for 14 years prior. At UCI, Eltawil worked in the Department of Electrical Engineering and Computer Science and was also the founder and director of the university’s Wireless Systems and Circuits Laboratory. As an expert in system integration for wireless systems, he joins KAUST with an established pedigree of university-industry collaboration, and he continues to act as an advisor in the development of wireless systems for leading international companies.

Meet KAUST prospective student: Yingquan Li

1 min read ·
Yingquan Li, 21, is an electrical engineering graduate who will join KAUST in the fall of 2020. He will join KAUST as a M.S./Ph.D. candidate in the KAUST Communication Theory Lab under the supervision of Professor Mohamed-Slim Alouini. An initial reference from a supervisor spurred his interest in joining KAUST, which resulted in a visiting student opportunity at KAUST. During his internship, Li was amazed by the facilities and research standard found on campus. It was this combination of impressive factors that inspired him to continue his academic career at the University.

Meet KAUST prospective student: Waleed Abu Laban

1 min read ·
Waleed Abu Laban is an electrical engineering graduate who will join KAUST this fall as a M.S./Ph.D. candidate and member of the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil.

Yao GE

Visiting Scholar with the Department of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST)

Xue Zhang

Xue Zhang is a Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Xue Zhang received her bachelor’s degree in Electronic Information Engineering from Southwest University in 2020 and received her master’s degree in Electronic Information from University of Electronic Science and Technology of China (UESTC) in 2023. Research Interest Xue Zhang is interested in signal processing and matrix optimization. Education Profile M.S. in Electronic Information

Waleed Abu Laban

Waleed Abu Laban is a M.S./Ph.D. candidate at the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil. Before joining KAUST, Waleed obtained a bachelor's degree in electrical engineering from The University of Jordan. Research Interests Waleed's research interests include signal processing and machine learning.

Vijith Varma Kotte

PhD student at the Information System Lab (ISL), in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Sofiene Barhoumi

Sofiene Barhoumi is an MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career In 2020 and after being ranked among the top 2% at the Tunisian National Exam for Entrance to Engineering Schools, Sofiene Barhoumi joined the most prestigious engineering school in Tunisia, "Ecole Polytechnique de Tunisie (EPT)" where he received multidisciplinary training. In his final year, he specialized in Signals and Systems and received his Engineering Diploma with first-class honors. Before joining

Shahzad Gishkori

Postdoctoral Research Fellow (former), Information Science Lab
Postdoctoral Research Fellow, Electrical and Computer Engineering , King Abdullah University of Science and Technology.

Sana Mazahir

Education and Early Career Sana Mazahir received the B.E. and M.S. degrees in Electrical Engineering from the College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), in 2013 and 2015, respectively. She worked at the System Analysis and Verification (SAVe) laboratory at NUST between 2015 and 2019. Currently, she is pursuing the Ph.D. degree at King Abdullah University of Science and Technology, Saudi Arabia, under the supervision of Prof. Mohamed-Slim Alouini. Her research interests include signal processing for wireless communications. Research

Rose Nasser Alaslani

MS Student, Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq Al-Naffouri.

Reema Alnafisi

M.S. student, advised by Professor Tareq Al-Naffouri under the Information System Lab (ISL). at King Abdullah University of Science and Technology (KAUST)

Peihao Li

Peihao Li is a Ph.D student in Electrical Engineering department within CEMSE division. He got a B.S in Observation, Navigation and Control from Northwestern Polytechnical University, China. He received master degree in the Ms/PhD program in Electrical Engineering at King Abdullah University of Science and Technology (KAUST) in winter 2017. His research interests include UAV navigating, image processing and robust control. Currently, he is working on developing algorithms and applications in signal processing and is especially interested in semi-classical signal analysis (SCSA). Research

Nouf Alamri

Education Nouf Alamri is currently pursuing a B.S. in electrical engineering at Boston University and is graduating in May 2020. Research Interests Nouf is interested in applied electromagnetics in general and signal processing. She is also exploring her interest in energy and systems related research.

Ngoc-Phuc Le

Postdoctoral Research Fellow, Electrical and Computer Engineering
Ngoc-Phuc Le is a postdoc research fellow in the Communication Theory Lab (CTL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Ngoc-Phuc graduated from Ho Chi Minh City University of Technology-Vietnam with the degrees of Bachelor of Engineering and Master of Engineering (all in Electronics- Telecommunications) in 2004 and 2006, respectively. He obtained his PhD degree in Telecommunications Engineering from the University of Wollongong-Australia in 2015. Prior to joining KAUST, he held several

Mohammad Tamim Alkhodary

Ph.D. degree in electrical engineering from the King Fahd University of Petroleum and Minerals. Postdoctoral Fellow at École de technologie supérieure, Montreal, QC, Canada

Mohamed Habib Loukil

MS Degree in the Communication Theory Lab (CTL) under the supervision of Professors Mohamed-Slim Alouini and Tareq Al-Naffouri at King Abdullah University of Science and Technology (KAUST).

Mohamed Abdelwaheb Bahloul

My name is Mohamed Abdelwaheb Bahloul, native from Sfax, Tunisia. I am a Ph.D. candidate in the Computer, Electrical, and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST). I first came to KAUST, in 2015, as an intern within the Visiting Student Research Program. Impressed with the University, its facilities, and the KAUST lifestyle, I then applied to KAUST as a master's degree student and was accepted to the electrical engineering program, electro-physics track. After graduating with my M.S. in 2017, I began my Ph.D. studies at KAUST

Mohamed A Suliman

Visiting Scholar (former), Information Science Lab
MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London..

Mohamed Amine Arfaoui

Mohamed Amine Arfaoui was a Visiting B.S. Student in CTL. Then, he joined Texas A&M University in Qatar as a Research Assistant. Education ​Diplome d’Ingenieur, Ecole Polytechnique de Tunisie (EPT), Tunis, Tunisia (Sep. 2012 - Jun. 2015). Education Profile ​Diplome d’Ingenieur, Ecole Polytechnique de Tunisie (EPT), Tunis, Tunisia (Sep. 2012 - Jun. 2015).

Maria Sara Nour SADOUN

Visiting Student (former), Information Science Lab
Research internship in King Abdullah University of Science and Technology within EMANG as a Visiting Student on the topic of Epileptic Seizure Detection using Electroencephalography (EEG).

Krishnendu S Tharakan

Visiting Student (former), Electrical and Computer Engineering
Visiting student at King Abdullah University of Science and Technology (KAUST).

Ismail Ben Atitallah

Ismail Ben Atitallah was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Ibrahim Almuqbil

Internship in King Abdullah University of Science and Technology (KAUST)

Ibrahim Alshehri

intern at King Abdullah University of Science and Technology(KAUST)

Heba Almorad

Visiting student in the Communication Theory Lab (CTL) and the Information System Lab (ISL), at King Abdullah University of Science and Technology,
Hakim

Hakim Jemaa

KAUST is an environment that promotes competitivity and encourages personal growth in a rich multicultural community.

Hadi Sarieddeen

postdoctoral fellow in the Communication Theory Lab (CTL) and the Information Science Lab (ISL) at KAUST, working under the supervision of Distinguished Prof. Mohamed-Slim Alouini and Prof. Tareq Al-Naffouri.

Furrukh Sana

Research Fellow, Harvard Medical School, September 2018 - present. Assistant Professor, National University, Pakistan, Ph.D. Degree King Abdullah University of Science and Technology,

Fahad Albalawi

​​ ​Fahad Albalawi is a research scientist in the Electrical Engineering department within the CEMSE division. He got an M.Sc. in Electrical Engineering from George Washington University, the United States where he worked in Process control theory, Robotics and Applied mathematics. He obtained his Ph.D. degree in Electrical Engineering from the University of California Los Angeles ( UCLA) where he worked in optimization, model predictive control, process operational safety, and system identification. He joined professor Meriem group at King Abdullah University of Science and Technology (KAUST)

Bodhibrata Mukhopadhyay

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bodhibrata Mukhopadhyay is a Postdoctoral Fellow at Computer Electrical and Mathematical Sciences and Engineering Center (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in Communication Theory Lab (CTL). Education and Early Career Bodhibrata Mukhopadhyay received his Master of Science (MS(R)) degree in Telecommunication and PhD degree in Electrical Engineering from the Indian Institute of Technology Delhi (IITD), India, in 2015 and 2021. Since 2017, he has been the Director of Suxma Systems (an IIT Delhi incubated startup), which

Bartłomiej Błeszyński

Visiting B.S. Student (former), Electrical and Computer Engineering
Education BSC Electronic Engineering (2014 - 2018), Warsaw University of Technology, Poland Research Interest Signal processing, analog circuits, RF engineering

Basit Olakunle Alawode

Visiting student at King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq AlNaffouri.

Asma Abdullah Sarouji

Visiting Student (former), Electrical and Computer Engineering
Research intern at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Tareq Al-Naffouri on health project that has a significant impact on people’s lives.

Asem Alalwan

Asem Alalwan is a Ph.D. candidate in the Estimation, Modeling, and Analysis group (EMAN) supervised by Prof. Meriem Laleg at King Abdullah University of Science and Technology (KAUST). Education and Early Career Asem has graduated with a bachelor degree in Electrical Engineering from King Fahad University of Petroleum and Minerals KFUPM) in 2014. He joined KAUST in 2014 and earned his Master degree in 2016. Research Interests Asem's research is focused in the area of control and design of Free space optical (FSO) communication systems. FSO has emerged as a viable technology that offers a large

Anum Ali

Intern (former), Information Science Lab
Research Engineer KAUST (King Abdullah University of Science and Technology).Senior engineer at Samsung Research America.

Alice Faisal

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST), chair of IEEE women in engineering affinity group at Effat University student branch.

Alam Zaib

Research Intern at King Abdullah University of Science and Technology (KAUST) ,Assistant Professor / COMSATS institute of information technology, Abbottabad.

Ahmed Mohammed Magbool

M.Sc. Degree in Electrical Engineering, King Abdullah University of Science and Technology (KAUST)

Ahmed Eltawil

Professor Eltawil is an expert in wireless communication system design with an established history of university-industry collaboration. He continues to act as an adviser in developing wireless systems for leading international companies.

Abdullah Ahmed Zayat

Visiting Student Research Program under the supervision of Professor Tareq Al-Naffouri ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is
<> cybersecurity (51)

Advancing Security Red-Teaming in Cyber-Physical Systems via AI-Driven Side-Channel Analysis

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B9 L3 R3223

This talk will systematically reveal security and privacy vulnerabilities in cyber-physical systems by characterizing contactless side-channel attacks on various components and proposing effective defense methods using hardware-software co-design and AI-driven techniques, while also outlining future research directions for developing secure and privacy-preserving CPS platforms.
Ali Hassan

Ali Hassan

Instructional Professor, Computer Science
Ali Hassan is an instructional professor in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at KAUST.
Ali Hassan

CEMSE New Faculty: Ali Hassan, Instructional Professor of Cybersecurity

2 min read ·
KAUST welcomes Professor Ali Hassan as an instructional faculty of Cybersecurity.

Security analysis of Docker-based containerized environments

Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various faults, such as container escape. Those faults might be manifested as host OS bugs, container runtime vulnerabilities, and/or poor container deployment choices and profile configuration. The latter aspect is particularly critical as

Monitoring containerized environments for security state error detection

Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various threats, such as container escape. In those threats, compromised or rogue containers might exploit existing vulnerabilities or poor container deployment choices to successfully inject security state errors (e.g., breaking out of the

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Breaking the Vehicle Over-The-Air Update System

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart

Divyanshu

Divyanshu, a PhD candidate in Electrical and Computer Engineering at KAUST, focuses on spin-controlled devices for hardware security primitives.

Adaptive Intrusion Management System for Databases

Muhamad Felemban, Assistant Professor, Computer Engineering Department, KFUPM

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B9 L2 H1

With the growing cyber-security threats to governmental and organizational infrastructures, the need to develop high resilient systems that preserve the security and privacy of data is becoming increasingly important. Although there is a large body of work on security and privacy countermeasures, cyber-attacks still persist. A prominent type of such attacks is intrusion attack that aims at data tampering, which can impair the availability and the integrity of data.

Resilient Computing: when Dependability meets Cybersecurity

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B9 L2 H2 R2325

Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.

Finding the linchpins of the dark web: Understanding and detecting malicious infrastructure

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA

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B9 L2 H1 R2322

In this talk, Sumayah will survey a number of malicious hosting infrastructures for different services and approaches to detecting them. Among them are works on an emerging trend of Bulletproof hosting services reselling infrastructure from lower-end service providers, use of residential proxy as a service to avoid server-side blocking and DNS based hosting infrastructure.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Multi-layer Red Team vs. Blue Team Methods towards Secure Cyber-Physical Energy Systems

Charalambos (Harrys) Konstantinou, Assistant Professor of Electrical and Computer Engineering with Florida A&M University and Florida State University (FAMU-FSU) College of Engineering

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B9 L2 H1

Election hacking, power grid cyber-attacks, troll farms, fake news, ransomware, and other terms have entered our daily vocabularies and are here to stay. Cybersecurity touches nearly every part of our daily lives. Most importantly, national security and economic vitality rely on a safe, resilient, and stable cyber-space. We rely on cyber-physical systems with hardware devices, software platforms, and network systems to connect, travel, communicate, power our homes, provide health care, run our economy, etc. However, cyber-threats and attacks have grown exponentially over the past years, exposing both corporate and personal data, disrupting critical operations, causing a public health and safety impact, and imposing high costs on the economy. In this talk, we will focus on cyber-physical energy systems (CPES) as the backbone of critical infrastructure, and provide a research perspective and present red team security threats, challenges, and blue team countermeasures. We will discuss recent approaches on developing low-budget targeted cyberattacks against CPES, designing resilient methods against false data, and the need for an accurate assessment environment achieved through the inclusion of hardware-in-the-loop testbeds.

Embedded and Cyber Connected Systems (ECCS) Workshop

Prof. Ahmed Eltawil, Prof. Charalambos Konstantinou, Prof. Khaled Nabil Salama

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B2 L5 R5220

The workshop aims to bring together experts to present their latest research efforts related to Embedded and Cyber Connected Systems architectures and platforms that can scale efficiently, as well as operate securely and resiliently to provide the necessary resources demanded by current and future network applications.

Cybersecurity of Industrial Control Systems - Challenges and Research Directions

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B9 L2 H2 H2.

This Distinguished Lecture is part of the CS Graduate Seminars. Abstract Industrial Control Systems play an important role in monitoring, controlling, and automating critical infrastructure such as water, gas, oil, and electricity. Recent years have seen a number of high-profile cyber attacks on such infrastructure, exemplified by Triton, Colonial Pipeline, and the Ukrainian Power Grid attacks. This naturally begs the question: how should we manage cyber security risks in such infrastructure on which the day-to-day functioning of our society relies? What are the complexities of managing

Leveraging Power Systems’ Modularity to support Resiliency Against Synthetic Cyber Disasters

Dr. Salam Baniahmed, Technical Manager, Veloce Energy

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B1 L4 R4214

The fact of power systems’ criticality to national security mandates continuous attention to the worst-case disruption scenarios. In a cyber world, grid modernization efforts have been hindered by cyber-related concerns. This webinar sheds some light on resiliency achievement by utilizing the modular cyber-physical nature of power systems, from an electron in a battery to a digital bit in the internet abyss.

Towards Cyber Resilient Autonomous Vehicle Ecosystems: Pitfalls and Enablers

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B9 L2 H2 H2

Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.

Security, Privacy, and Human

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B4 L5 R5209

Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.

Scheduling tasks in energy harvesting IoT

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B4 L5 R5209

Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.

Memory Forensics 2.0

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B9 L2 H2 H2

The risk of security breaches is now higher than ever, and attackers routinely break into corporate networks, government services, and even critical infrastructures. As a result, it is not a matter of `if' a system will be compromised, but only a matter of `when' -- thus making the way we handle computer incidents and investigations of paramount importance.

The SCION Research and Education Network

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B9 L2 H2 H2

Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.

Security and privacy in centralized and decentralized machine learning

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B4 L5 R5209

Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.

RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

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B5 L5 R5220

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

Towards Transparency of IoT Device Presence

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B9 L2 H2 H2

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.

Data, AI and Cybersecurity: Synergies and Interconnection

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B9 L2 H2 H2

This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Securing Cyber-Physical and IoT Systems in Smart Living Environments

Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.

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B4 L5 R5220

Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.

Dependability assessment work at City, University of London and possible synergies with RC3 at KAUST

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B4 L5 R5209

Abstract The talk will give an overview of research at the Department of Computer Science at the City University of London, focusing on the past and current dependability work at the Centre for Software Reliability. The talk will conclude with an overview of related research that is continuing or planned to start at City in 2024 and possible synergies and opportunities for collaboration with RC3 at KAUST. Brief Biography Ilir Gashi holds PhD (2007) and BEng (Honours) (2003) degrees in Software Reliability and Computing, respectively, from the City, University of London, where he is currently a

Exploring the Human Element in Combating Malicious Software Threats

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B9 L2 H2

Malicious software or malware is a serious cybersecurity threat and the research community has explored it extensively for almost three decades. Since it is believed that people are often the weak link in cybersecurity, exploring malware attacks and defenses in the human context can provide new insights into how the threat posed by malware can be addressed.

ML-based Network Intrusion Detection Systems: Resilience threats and a mitigation approach

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B4 L5 R5220

With the ever-increasing amount of cyberthreats out there, securing IT and OT infrastructures against these threats has become not only desirable, but fundamental. Network Intrusion Detection Systems (NIDS) are key assets for system protection, providing early alerts of network attacks. An important class of NIDS are those based on ML techniques, around which a substantial amount of research is being done these days. Unfortunately, being ML-based, these NIDS can be targeted by adversarial evasion attacks (AEA), which malicious parties try to exploit to perform network attacks without being detected.

KAUST Associate Professor Andrea Fratalocchi’s research leads to Institute of Physics Fellowship

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering (CEMSE) division, was recently granted a Fellowship of the Institute of Physics (FInstP) by the Institute of Physics (IOP). Fratalocchi was awarded the IOP's highest level of membership in recognition of his outstanding accomplishments in physics.

Meet KAUST new student: Ioannis Zografopoulos

1 min read ·
Ioannis Zografopoulos is a Ph.D. student who joined KAUST this fall as a Ph.D. candidate and member of the Secure Next Generation Resilient Systems (SENTRY) Lab under the supervision of Professor Charalambos Konstantinou. Zografopoulos believes that KAUST's renowned faculty, global reputation and research infrastructure make the University the ideal environment for him to develop high-quality, impactful research.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

KAUST SENTRY team wins 2021 Cybersecurity Games & Conference Embedded Security Challenge

1 min read ·
The SENTRY Lab team wins the 18th annual CSAW Cybersecurity Games & Conference’s (CSAW '21) Embedded Security Challenge (ESC).

Blocking microgrid cyberattacks to keep the power flowing

1 min read ·
Power grids have become far more complex in recent decades due to energy demands, environmental regulations and small-scale renewable energy systems that turn businesses and individuals into combined consumer-producers. One way to ensure that power supplies remain resilient is to create small groups of sources and loads called microgrids. Microgrids can operate independently of the main grid when required, such as supporting hospitals during natural disasters, for example. As microgrids grow more complex, they require sophisticated computer networks to coordinate, control and distribute

Simple hardware to defend microgrid attacks

1 min read ·
An inexpensive piece of hardware integrated with solar panel controllers can protect isolated power networks from cyberattacks.

Xiaojie Zhu

Xiaojie Zhu is a staff research scientist at KAUST. Prior to that, he was an assistant professor of cybersecurity in the Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University. He received his Ph.D. from the Department of Computer Science, University of Oslo, Norway. Xiaojie's research interests are data security and privacy, software security, decentralized trust and infrastructure, and applied cryptography. He has published in prestigious journals such as IEEE TDSC, IEEE TPDS, JNCA, and JOS. One of his articles was included in the 21st annual

Roberto Di Pietro

Professor, Computer Science
Professor Di Pietro is a cybersecurity expert who excels in building and leading research and innovation labs in the private sector, government and academia. He works on critical infrastructure protection, adopting an artificial intelligence-driven approach in collaboration with major stakeholders.

Michał Forystek

M.S. Student, Computer Science
Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 IT Specialist with a background in quantum engineering and data science. He leverages analytical, multidisciplinary, and data-driven approaches to drive innovation and efficiency within the IT infrastructure. He excels at full-stack development, identifying complex problems' root causes and developing impactful long-term solutions within a broad area of competence.

Marc Dacier

Professor, Computer Science
Leveraging 20+ years of international experience, Professor Dacier leads the charge in uncovering internet vulnerabilities and developing robust defenses, building a more secure digital future for all.

Mahmoud Attia

M.S. Student, Computer Science
Mahmoud Attia is currently an MS/PHD student working with the SeRBER research group led by Prof. Marc Dacier. His research interests are in network security, within the areas of IT and OT security. He previously joined KAUST as a visiting student in the summer of 2023.

Ioannis (Yannis) Zografopoulos

Ioannis Zografopoulos received his Ph.D. at the Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE) of King Abdullah University of Science and Technology (KAUST) in 2023. Prior, he graduated with a B.Eng. and M.Eng. degrees in Computer, Communications, and Network Engineering, and an M.Sc. degree in Electrical and Computer Engineering from the University of Thessaly, Volos, Greece, in 2014 and 2015, respectively. His research interests include cyber-physical systems security, with an emphasis on embedded systems for industrial, distributed energy, and power grid

Ines Pinto Gouveia

Postdoctoral Research Fellow, Cyber Resilience Research Group
Working towards building safe, secure and resilient systems.

Ilies Benhabbour

Ilies is working with professor Marc Dacier on the following subject: Fingerprinting of intra network semi active components in end-to-end communications. That is to say that we want to be able to identify components which may exist between two devices. It can be either something normal like a firewall or a proxy, but it can also be something malicious like a man in the middle attack. The project's goal will thus be to identify how we can do so, and what kind of experiments can be done over the packets to see if these semi active components exist. Another aim would be to create some code that

Hugo Bertin

Hugo is a Visiting Student from the University of Rennes, France. He is interested in the network and system aspects inherent in distributed systems, which often involve a trade-off between security and performance. He joined the SeRBER research group to work on EGaming Security under the supervision of Professor Marc Dacier. The research project aims to investigate the security aspects leveraged by the gaming industry, which experienced unprecedented growth, especially with the surge of Esports and is expected to continue to shape tomorrow's virtual worlds. Massively Multiplayer Online Games

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Elisa Chiapponi

I am currently a PhD student in Digital Security enrolled in the Sorbonne Université. I work on my research project at EURECOM and Amadeus, under the supervision of prof. Marc Dacier at KAUST My areas of interest include Network and Web security. My thesis focuses on the analysis and mitigation of the new generation of botnets, in particular the ones performing web scraping. My goal is to find practical means to defeat them. The starting point of my project is understanding the ecosystem of scraping bots. This consists of identifying the actors behind this business and their motivations

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Alya AlShaikh

Alya AlShaikh is currently an undergraduate student at Alfaisal University in Riyadh, KSA. She is pursuing a Software Engineering BSc specializing in Cyber Security and Machine Learning. Alya joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Konstantinou as an Accelerated Bachelor’s and Master's (ABM) student. Her research interests include applying machine learning techniques to enhance cyber defense strategies.

Ali Shoker

Research Associate Professor, Computer Science
Ali Shoker, a Research Associate Professor at KAUST, explores Byzantine fault tolerance, AI, and blockchain to create practical solutions for autonomous vehicles, fintech, and satellite systems.

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> photonics (49)

Future of Semiconductors Forum 2025 | KAUST

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Building 20

Join global leaders at KAUST to shape the next era of semiconductor technologies.

Professor Mohamed-Slim Alouini named a Fellow of SPIE and a Distinguished Lecturer of the IEEE Photonics Society

4 min read ·
Professor Mohamed-Slim Alouini earns SPIE Fellow and IEEE Distinguished Lecturer titles, highlighting his influential contributions to optical wireless communications.

EE Seminar: AMMONO-GaN: ultimate substrate solution for energy saving GaN power electronics

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B3 L5 R5220

Bulk gallium nitride (GaN) crystals are regarded as the most promising candidates for substrates for optoelectronic, high power and high frequency electronic devices. After dacedes of extensive research on a numer of methods incluging HNPS, HVPE and Flux - ammonothermal (AMMONO) method of bulk GaN growth offers the best combination of crystal quality and size, within foreseen scalability able to meet huge demands of lighting and power electronic markets. Developed as analogy of hydrothermal method commonly used for multi-ton production of synthetic quartz, AMMONO method currently provides 2-inch crystals of excellent structural properties and wide spectrum of electrical parameters.

Photonic topological phases in dispersive metamaterials

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B1 L4 R4214

We analyze the photonic topological phases in dispersive metamaterials which satisfy the degenerate condition at a reference frequency, where the hybrid modes are decoupled and determined by two subsystems with degenerate eigenvalues. By introducing the pseudospin states as the eigenfield basis, the Hamiltonians of the hybrid modes represent the pesudospin-orbit interaction with spin 1, which result in nonzero spin Chern numbers that characterize the topological phases.

Quantum Inspired Photonics

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B1 L2 R4214

Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.

Photonics Summer Camp 2019 - The Final Presentations

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B2 B3 A0215

International Students in CEMSE who attended Photonics Summer Camp 2019, will present their research findings and experience of KAUST, in 10 minute presentations. Breakfast will be served. All KAUST affiliates are welcome to attend. The Photonics Summer Camp is an international internship program, currently in it’s 4th year. The program is just 4 weeks long and is designed to welcome overseas and local students to KAUST, in order to facilitate research innovation and collaboration amongst the next generation of photonics researchers. The program is sponsored by the CEMSE Division, with support from Graduate Affairs, and the International Programs Office.

Study of ultraviolet AlGaN nanowires light-emitting diodes

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B3 L5 R5209

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Deep-Ultraviolet Optoelectronic Devices Enabled by the Hybrid Integration of Next-Generation Semiconductors and Emerging Device Platforms

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B3 L5 R5209

In this dissertation, the design and fabrication of deep-ultraviolet photodetectors, based on gallium oxide and its alloys, through the heterogeneous integration with metallic and other inorganic materials is investigated. The crystallographic properties of grown oxide films formed directly and indirectly on silicon, magnesium oxide, and sapphire are examined, and the challenges that hinder the realization of efficient and reliable deep-ultraviolet photodetectors are elaborated on. I provide an overview of aluminum nitride, gallium oxide, sapphire, and silicon substrates as platforms for deep-ultraviolet optoelectronic devices, in which I elaborate on the challenges associated with using sapphire as a platform for efficient deep-ultraviolet devices and detail advancements in device growth and fabrication on silicon and magnesium oxide substrates.

Ultraviolet band based underwater wireless optical communication

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KAUST

Underwater wireless optical communication (UWOC) has attracted increasing interest for data transfer in various underwater activities, due to its order-of-magnitude higher bandwidth compared to conventional acoustic and radio-frequency (RF) technologies. Our studies pave the way for eventual applications of UWOC by relieving the strict requirements on PAT using UV-based NLOS. Such modality is much sought-after for implementing robust, secure, and high-speed UWOC links in harsh oceanic environments. This work was first started with the investigation of proper NLOS configurations. Path loss (PL) was chosen as a figure-of-merit for link performance. The effects of NLOS geometries, water turbidity, and transmission wavelength are evaluated by measuring the corresponding PL. The experimental results suggest that NLOS UWOC links are favorable for smaller azimuth angles, stronger water turbidity, and shorter transmission wavelength, as exemplified by the use of 375-nm wavelength. With the understanding of favorable NLOS UWOC configurations, we established a NLOS link consisting of an ultraviolet (UV) laser as the transmitter for enhanced light scattering and high sensitivity photomultiplier tube (PMT) as the receiver. A high data rate of 85 Mbit/s using on-off keying (OOK) in a 30-cm emulated highly turbid harbor water is demonstrated. Besides the underwater communication links, UV-based NLOS is also appealing to be the signal carrier for direct communication across wavy water-air interface. The trial results indicate link stability, which alleviates the issues brought about by the misalignment and mobility in harsh environments, paving the way towards real applications.

The fourth wave: ultrawide bandgap compound semiconductors for photonics and electronics

Xiaohang Li, Assistant Professor, Electrical and Computer Engineering

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KAUST

Wide bandgap (WBG) compound semiconductors including GaN have shown enormous success in solid-state lighting, display, and electrification in recent decades due to superior properties such as direct bandgap, high electron mobility, and large breakdown field. They have been changing the world by elevating living standards and addressing grand challenges such as global warming. The pioneering researchers have been recognized by numerous accolades including the Nobel Prize and most recently, the Queen Elizabeth Prize. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) compound semiconductors with bandgap larger than 3.4 eV have attracted increasing attentions: they have been regarded as the 4th wave/generation after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, respectively, both crucial for human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. However, extensive multi-disciplinary studies of growth, materials, physics, and devices are essential to unearth the potentials due to the infancy. This seminar would cover the latest research on those aspects. It includes growth of state-of-the-art materials, discovery of unique material properties, and development of a widely adopted device physics framework for photonics and electronics especially short and long wavelength photonic devices.

Machine learning in hardware via trained metasurface encoders: theory, design and applications

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B2 L5 R5209

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Photonics Design and Remote Sensing

Prof. Hugo Enrique Hernandez Figueroa

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B18 L4 Fellowship Hall

Abstract This tutorial series will include: Seminar 1 (Monday, 2PM) – Electromagnetic Numerical Modeling A general view of electromagnetic numerical modeling. Finite Element Method (FEM) vs Finite Difference Method. FEM formulations for solving Maxwell equations: time-domain and frequencydomain. Scattering, resonant structures and guiding modeling. Description of NEO-PZ: LEMAC’s electromagnetic simulator package. Challenging electromagnetic problems: very large electrical spatial domains demanding very high localized accuracy. Deterministic and heuristic optimization techniques and artificial

KAUST-Fudan Joint Workshop on Intelligent Communication, Device and System Technologies

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B18 L4 Fellowship Hall

This joint workshop between KAUST and Fudan University covers a variety of topics related to optical communication and devices for visible light communication. Theoretical modeling, device engine/materials investigation and sub-system development will be discussed. These research are essential to eventually realize the goals of connecting all the unconnected, SMART systems and optical internet-of-things. The workshop will be running in hybrid mode, and presenters will be from both KAUST and Fudan University.

KAUST Masterclass: Semiconductor Photonics

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KAUST

Semiconductor Photonics is an immersive master class that explores the cutting-edge field of advanced optoelectronics and its diverse applications. This class brings together experts and enthusiasts to delve into the forefront of research and innovation in semiconductor photonics. Key topics include Semiconductor Laser-Based Lighting and Visible Light Communications, Artificial Intelligent Optoelectronics Hardware for Neuromorphic Computing, Optoelectronic Micro-Devices for Novel AR and VR Displays, and Integrated Silicon Photonics. By covering these varied areas, the class offers a comprehensive understanding of semiconductor photonics and its implications in information technology, healthcare, computing, and beyond.

Gbps Laser-based underwater wireless optical communication

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B9 L2 H2

I will focus my discussion on optoelectronic devices and system challenges facing long-distance, multiple-Gbps underwater wireless optical communication. The future perspective of underwater photonics will also be discussed.

Prof. Andrea Fratalocchi receives appreciation from Nature Publications as one of its best reviewers

1 min read ·
Prof. Andrea Fratalocchi received a letter of appreciation from the Editor-in-Chief of Nature for serving as a reviewer.

Mohammed Zahed M. Khan (EE PhD Candidate) and Dr. Boon Ooi (Professor of EE) won the Best Paper Award (Photonics Track) at the SIECP conference

1 min read ·
Mohammed Zahed M. Khan (EE PhD Candidate) and Dr. Boon Ooi (Professor of EE) won the Best Paper Award (Photonics Track) in the second SIECPC.

Klemens Katterbauer (PhD in Electrical Engineering) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper contest

1 min read ·
Klemens Katterbauer (Ph.D. candidate under supervision Andrea Fratalocchi) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper Contest.

Tsunami On Demand: The Power To Harness Catastrophic Events

1 min read ·
A new study published today on Nature Physics features a nano-optical chip that makes possible generating and controlling nanoscale rogue waves. The innovative chip was developed by an international team of physicists, led by Andrea Fratalocchi from King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, and is expected to have significant applications for energy research and environmental safety.

The Orange Laser

1 min read ·
In the last issue of Electronics Letters of 9th July 2015 Vol.51 No.14 a team of researchers coordinated by Prof. Boon Ooi, director the Photonics Laboratory at King Abdullah University of Science & Technology (KAUST), presented a phosphide-based laser structure that can operate in the orange wavelengths at room temperature.

Cancer screening advance: single point genetic mutations made visible by new optical chip

1 min read ·
The collaboration of the teams of professors Enzo Di Fabrizio and Andrea Fratalocchi at the King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, has led to the development of a new device that enables the detection of mutations down to a single amino acid.

The International Year of Light Open Day

1 min read ·
The International Year of Light (IYL) Open Day event took place at KAUST on December 3, 2015. The event was designed to celebrate light’s impact on human welfare as well as the importance of light as one of the most challenging fields of research in contemporary science. The Open Day Exhibition showcased the latest technological developments in light research, with special emphasis on photonics and LED-based technologies and the innovative applications that they will bring to the energy and communication sectors in the future. The exhibit experiments and the striking materials were provided by

CEMSE’s Professor Boon Ooi appointed Fellow of the Optical Society of America (OSA)

1 min read ·
Boon Ooi, Professor of Electrical Engineering and Director of the KACST - Technology Innovation Center for Solid-State Lighting, under the Computer, Electrical and Mathematical Sciences and Engineering Division of KAUST, was recently appointed Fellow of the Optical Society of America (OSA).

VSRP intern Muwei Zhang's first-author paper hits the headline of the Physica Status Solidi Journal

1 min read ·
Professor Xiaohang Li's Visiting Student Research Internship Program (VSRP) intern, Muwei Zhang has recently published his first-author paper "Structural and electronic properties of wurtzite BxAl1_xN from first-principles calculations" on the Physica Status Solidi (b) Journal.

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE - call 3 - 2018

1 min read ·
‌ A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska- Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY. Candidates are invited to submit proposals for research within the environment of laboratories of professors Andrea Fratalocchi , Boon S. Ooi, Carlo Liberale, Enzo M. Di Fabrizio, Kazuhiro Ohkawa, Mohamed-Slim Alouini, and Xiaohang Li. The call deadline is the 2nd of April 2018. For application information please check the call flyer or

UESTC students visit CEMSE Laboratories during a one week Winter Camp at KAUST

1 min read ·
Twenty-two undergraduate students from the University of Electronic Science and Technology of China (UESTC), Chengdu, are currently visiting the CEMSE Division as part of one week Winter Camp at KAUST.

Ph.D. student Valerio Mazzone wins best paper award

1 min read ·
Valerio Mazzone, a Ph.D. student in the Primalight group based in the University's Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), was awarded the best paper award at the 9th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META).

KAUST Associate Professor Andrea Fratalocchi’s research leads to Institute of Physics Fellowship

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering (CEMSE) division, was recently granted a Fellowship of the Institute of Physics (FInstP) by the Institute of Physics (IOP). Fratalocchi was awarded the IOP's highest level of membership in recognition of his outstanding accomplishments in physics.

Andrea Fratalocchi Elected OSA Fellow for Pioneering Innovations

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently elected as a Fellow Member of The Optical Society of America (OSA) at the Society’s Board of Directors meeting in September.

KAUST Associate Professor Andrea Fratalocchi elected Fellow of the Optical Society

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering division, was recently elected as a Fellow of the Optical Society (OSA) at the society's Board of Directors meeting in September.

KAUST Ph.D. student receives SPIE 2020 Optics and Photonics Education Scholarship

1 min read ·
Ronghui Lin, a Ph.D. student under the supervision of Professor Xiaohang Li, has been awarded an SPIE 2020 Optics and Photonics Education Scholarship for his “potential long-range contribution to optics, photonics, or other related fields.”

Chun Hong Kang receives SPIE Optics and Photonics Education Scholarship

1 min read ·
Chun Hong Kang, a Ph.D. student in the KAUST Photonics Laboratory, has been awarded a 2021 Optics and Photonics Education Scholarship by SPIE for his “potential contributions to the field of optics and photonics.”

Outstanding KAUST research acknowledged by leading photonics journal

1 min read ·
KAUST professors Boon Ooi and Osman Bakr and their respective research groups have recently had their collaborative research recognized by one of the major journals for optics and photonics, Light: Science & Applications (LSA). The teams’ paper— titled "High-speed color-converting photodetector with all-inorganic CsPbBr3 perovskite nanocrystals for ultraviolet light communication—" was awarded as an “Outstanding Paper of Light: Science & Applications in 2020.”

Professor Ooi appointed Editor-in-Chief of IEEE Photonics Technology Letters

1 min read ·
KAUST Professor of Electrical and Computer Engineering Boon S. Ooi has been appointed Editor-in-Chief (EiC) of the IEEE Photonics Technology Letters beginning January 1, 2022.

KAUST New Faculty: Yating Wan, Assistant Professor of Electrical and Computer Engineering

1 min read ·
Yating Wan, assistant professor of electrical and computer engineering, joins KAUST as a member of the KAUST Photonics Laboratory from her most recent position as a postdoctoral research associate at the University of California, Santa Barbara (UCSB), U.S.

Zhirun Li

Zhirun Li is a M.S./Ph.D. student at King Abdullah University of Science and Technology (KAUST). He obtained bachelor degree of Optoelectronic Information Science and Engineering from University of Electronic Science and Technology of China (UESTC) in 2019. Education Profile M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2020 - present B Eng. in Optoelectronic Information Science and Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2015 - Jun. 2019
Yating Wan

Yating Wan

Dr. Yating Wan, an assistant professor of electrical and computer engineering at KAUST, excels in silicon photonics, with special emphasis on the integration of on-chip light sources.

Xiaobin Sun

Xiaobin Sun is a Ph.D. student at Photonics Laboratory under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). Research Interests Sun's research interests include Semiconductor Optoelectronic device, Optical communication, including underwater optical wireless communications, visible-light communications. Invited Talks: Visible diode lasers for high bitrate underwater wireless optical communications Boon S. Ooi, Xiaobin Sun, Omar Alkhazragi, Yujian Guo, Tien Khee Ng, Mohamed-Slim Alouini Optical Fiber Communication Conference, M3I. 1 (OFC 2019

Valerio Mazzone

Valerio Mazzone is a PhD student at the King Abdullah University of Science and Technology, where he joined Andrea Fratalocchi’s group, Primalight, since summer 2014. Valerio graduated in physics at University of Rome “La Sapienza”, Italy, with a MS Thesis about a computational study of DNA translocation in nanopore. His BS and MS studies focused on condensed matter and many-body systems. Before joining KAUST he collaborated with prof. Walter Schirmacher on a theoretical study of viscoelesticity of glass using coherent potential approximation. His current work regards the possibility to

Shahad Ali

Visiting Scholar, Photonics Laboratory
Shahad Ali was an SRSI (Saudi Research Science Institute) student at the Photonics Laboratory of Professor Boon Ooi. Currently, he is a BSc student at Pennsylvania State University, USA. Research Interests Shahad's research interests included ​​​Photonics.

Mohammed Zahed Mustafa Khan

Postdoctoral Research Fellow, Photonics Laboratory
Mohammed Zahed Mustafa Khan worked as a Post-Doctoral fellow at the Photonics lab after receiving his Ph.D. degree in Electrical Engineering under the supervision of Prof. Boon Ooi from King Abdullah University of Science and Technology (KAUST). Mohammed received his Master’s degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in 2004, and Bachelor’s degree in Electronics and Communication from Osmania University, India, in 2001. Mohammed is currently an Assistant Professor at King Fahd University of Petroleum and Minerals, Saudi Arabia

Mohamed Ebaid

Postdoctoral Research Fellow, Photonics Laboratory
Currently works at ​Lawrence Berkeley National Lab, California, USA

Hakan Bagci

Professor Bagci is a highly accomplished researcher in the field of computational electromagnetics (CEM). His research focuses on the theoretical and applied aspects of CEM.

Edgars Stegenburgs

Edgars Stegenburgs joined the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division of KAUST as an MS/Ph.D. student and graduate researcher at Photonics Laboratory in January 2015. Previously he was working in the Laser Centre of the University of Latvia as a natural sciences laboratory assistant under the supervision of Dr. Aigars Ekers in the Laser-manipulation laboratory. There he developed his bachelor thesis "Formation of bright and dark states in non-degenerate quantum systems and ultra-high vacuum system design for Rb atoms magneto-optical trap" and earned the

Daniel Toth

When Daniel Toth, MS '13 made the decision to attend KAUST he had several aims. The first was to complete another Master's degree; the second was to meet like-minded people with diverse backgrounds; and the third was to have an adventure exploring the unique region around KAUST. Daniel is pleased to report that his experience at KAUST more than fulfilled his expectations. Currently working as a PhD student in Switzerland, Daniel holds a Bachelor of Science (Physics) from Eötvös Loránd University in Hungary; a Master's degree in Theoretical Physics and Applied Mathematics from the University of

Boon Ooi

Professor Ooi is a pioneer in optoelectronics and photonic integrated circuits development, particularly for optical communications and sensor applications. His work has led to the successful commercialization of semiconductor lasers, broadband light sources, and optical sensors

Bilal Janjua

​Bilal Janjua obtained a Ph.D. degree in Electrical Engineering under the supervision of Professor Boon Ooi at the King Abdullah University of Science and Technology (KAUST). Previously, 2008-2010, Bilal worked as a research scientist, in Prof Seth Bank group at the University of Texas at Austin (UT Austin). During this period at UT Austin, he helped the group set up an MBE system to study rare earth elements in the III-V materials system. For a semester, in his master’s program, Bilal worked as a Teaching Assistant for a graduate course on semiconductor nanostructures. Bilal received his MSE

Andrea Fratalocchi

Professor Fratalocchi is a pioneer in complex systems and sustainable technologies, focused on addressing critical global challenges. His groundbreaking research has led to innovative solutions in areas such as energy harvesting, clean water production and smart materials. As a founder of the ECE program at KAUST and co-founder of Pixeltra, Professor Fratalocchi is a proven leader and entrepreneur, successfully translating his research into real-world impact.

Aigars Ekers

Short bio Dr. Ekers obtained his MS and PhD in physics from the University of Latvia in 1994 and 1999, respectively. During the PhD studies, he spent periods of time performing experiments at Lund University (Sweden), Warsaw University (Poland), and Kaiserslautern University (Germany), the latter as a DAAD research scholarship holder. After a successful proposal to EU Framework Programme, he returned to Kaiserslautern University as a Marie Curie Fellowship holder (1999-2002). After the Fellowship, he continued in Kaiserslautern as a research associate. In 2005, he moved to Riga to assume a
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Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.
Rihab Larayedh

Rihab Larayedh

Dedicated researcher in underwater acoustics , specifically investigating shipping noise from vessels, to enhance our understanding of its environmental impact and develop strategies for noise mitigation

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Numerical Approximation of the Stochastic Cahn-Hilliard Equation and the (Stochastic) Hele-Shaw Problem

B1 L2 R2202

The Cahn-Hilliard equation is a fourth order parabolic partial differential equation (PDE) that is widely used as a phenomenological model to describe the evolution of interfaces in many practical problems, such as, the microstructure formation in materials, fluid flow, etc. It has been observed in the engineering literature that the stochastic version of the Cahn-Hilliard equation provides a better description of the experimentally observed evolution of complex microstructure.

Modelling and Simulation of Biological Systems - 2022-11-28

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B2 L5 R5209

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

The role of applied mathematics in finance

1 min read ·
“Sometimes there’s a strange dichotomy between applied mathematics and pure mathematics,” said Professor Raul Tempone, a founding KAUST faculty member and principal investigator of the University's Stochastic Numerics Research Group. “People think that applied math is simply the application of theory.” The argument is that applied math is just classified by its ends in the sense that one is trying to solve a real problem. Tempone argues that this doesn’t mean that new theories don’t need to be created. It also does not mean that the kinds of problems are less challenging than the ones faced in

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Aesthetic patterns for freeform architecture

1 min read ·
Planar designs are used to model complex curved surfaces, with applications from architecture to 3D prints.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

1 min read ·
A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Tunable sound transmission shapes up

1 min read ·
Carefully designed crystal structures can now be tuned to control how they transmit acoustic waves.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

Getting the jump on industrial failures

1 min read ·
A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Blind matchmaking for more efficient wireless networks

1 min read ·
Autonomously pairing network users could expand the capability of the next generation of wireless networks

Signaling success for 5th gen communications

1 min read ·
An efficient wireless signal optimization scheme achieves peak performance for next-generation mobile communications.

Getting a visual on complex data

1 min read ·
A new method for visually presenting complex data distributions provides a much-needed tool for management, analysis and interpretation.

Tiny speakers break a barrier for sound

1 min read ·
A loudspeaker design could allow small devices to produce powerful low-frequency sounds.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.

Quieter wind beneath the wings

1 min read ·
The ability to efficiently simulate the noise generated by wings and propellers promises to accelerate the development of quieter aircraft and turbines.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Mathematician Peter Markowich named 2022 AMS Fellow

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich joins 45 mathematical scientists from around the world named 2022 Fellows of the American Mathematical Society (AMS) program.

Professor Athanasios Tzavaras elected Fellow of the European Academy of Sciences (EurASc)

1 min read ·
Athanasios (Thanos) Tzavaras has been elected Fellow of the European Academy of Sciences (EurASc). He was nominated for his contributions to the interface of nonlinear partial differential equations and applied mathematics in the physical sciences.

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Mohammed Sayyari

Mohammed is a Mathematician with a solid background in scientific programming. Mohammed's mathematical interests are in the analysis of partial differential equations and the discretization thereof.

Linda Alzaben

Linda Alzaben is a Ph.D. candidate in Applied Mathematics and Computational Science. Linda has a Bachelor's and a Master's degree, both in pure Mathematics, from King Abdulaziz University. In both her B.Sc. and M.Sc. she was first-class honors. Although she had been teaching for several years she decided to pursue her postgraduate studies in an engaging and interesting field at KAUST. She seized the opportunity to study and work under the supervision of Professor Daniele Boffi who is a distinguished professor in his field. Her main interest are in Finite elements and Mixed Finite elements and

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.

Georgiy L. Stenchikov

Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.

Aimad ER-RAIY

Aimad Er-Raiy joined the Extreme Computing Research Center (ECRC) at King Abdullah University of Science & Technology (KAUST) as a postdoctoral fellow in late 2019. He is working with Professor Matteo Parsani. Prior to this, he spent a year as a Postdoctoral Research Fellow in CERFACS (Toulouse, France). Education and Early Career Dr. Er-Raiy received a MSc. (2014) from the National Institute of Posts and Telecommunications (INPT) in Toulouse, France, and a Ph.D. (2018) from Institut Pprime & National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers (France). Research
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Bio2Vec Kickoff Meeting

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Paris France

​​Research Scientist Senay Kafkas will be attending the Bio2Vec kickoff meeting in Paris, France.

Big Data Analyses in Evolutionary Biology

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B9 H2

This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

Paving the Way for Next Generation Wireless Data Center Networks

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B1 L4 R4214

As big data, articial intelligence, cloud services, cellular infrastructure, content delivery; all of which entail interconnected and sophisticated computing and storage resources. Recent studies on traditional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack trac, and unidentied ow multi-classes best known as delay sensitive mice ow (MF) and throughput-hungry elephant ow (EF).

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

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B1 L4 R4102

Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

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KAUST

Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

EF-BV: distributed optimization with compressed communication

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B9 L2 R2322 H1

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Exploring science's fourth paradigm

1 min read ·
The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Mixing precision for model acceleration

1 min read ·
A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Code of collaboration

1 min read ·
David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.

Zuhair Yarub Khayyat

Zuhair Yarub Khayyat obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interest Zuhair's research interests included Big Data Management, Parallel and distributed data processing, Cloud Computing and Graph Analytics. His main experience was focused on problem-solving and algorithm development for processing large data in distributed environments. He is a reliable team player with a methodological mindset with the ability to utilize

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Kadir Akbudak

Dr. Kadir Akbudak is a Consultant at Extreme Computing Research Center at KAUST, working with Professor David E. Keyes and Dr. Hatem Ltaief. Research Interests Kadir Akbudak's research interests include Combinatorial scientific computing, Graph and hypergraph partitioning, Locality-aware partitioning and scheduling, High performance kernels for sparse computations, Vectorization for irregular applications, Iterative numerical methods and Parallel numerical algorithms. Education Profile Ph.D., Computer Engineering, Bilkent University, 2015. M.S., Computer Engineering, Bilkent University, 2009

Ashraf M. Kibriya

Research Interests ​My research interests include the application of Big Data and Machine Learning techniques to real-world problems in various domains, including biology and biomedicine. In particular, I'm interested in Deep Learning, Text Mining, Network Analysis and Graph Mining. Professional Profile ​2018-Present: Postdoctoral Fellow, KAUST, Thuwal, Saudi Arabia 2011-2018: Doctoral Fellow, KU Leuven, Belgium 2010-2011: Pre-doctoral fellow, KU Leuven, Belgium 2007-2009: Database developer / Datamining specialist, Datamine Ltd., New Zealand 2002-2005: Research Programmer, Machine Learning

Amer AlGhadhban

Amer AlGhadhban is a Ph.D. student under the supervision of Professor Basem Shihada at Networking Research Lab in King Abdullah University of Science and Technology (KAUST). Amer obtained both of his degrees B.S. and M.Sc. in Computer Engineering from King Fahd University of Petroleum and Minerals (KFUPM). He was a Cisco Academy instructor and during that he earned multiple professional certificates: Cisco Certified Network Associate (CCNA), Cisco wireless, SANS-GIAC Certified Firewall Analyst (GCFW-Gold), Certified Ethical Hacker (CEH), Security Certified Network Engineer (SCNP), and SANS

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for
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Professor Wolfgang Heidrich elected as a Fellow of the National Academy of Inventors and named a 2025 Optica Fellow

5 min read ·
KAUST Professor Wolfgang Heidrich has been named a Fellow of both the National Academy of Inventors (NAI) and Optica, recognizing his pioneering contributions to computational imaging, display systems and AI-driven optical technologies.

Discrete Laplacians for Polygons and Polyhedral

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B4 L5 R5209

Being able to accurately solve PDEs on arbitrary polygonal/polyhedral meshes is a central goal and has been considered for various differential operators over the last years. In this talk I will present a simple approach for computing (piecewise) linear and quadratic basis functions for general polygons and polyhedra, from which discrete operators for gradient, divergence and Laplacian can be derived.

Web-based 3D Visualization

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B9 L2 H2 H2

In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.

Humboldt Research Award for Prof. Heidrich

1 min read ·
The Alexander von Humboldt Foundation has awarded a Humboldt Research Award to Prof. Wolfgang Heidrich.

Alumni Focus: Ronell Sicat

1 min read ·
As the volume and complexity of data captured around the world continues to grow exponentially, new ways of exploring and visualizing this data are required. Today, society has moved beyond the traditional desktop computer with tools such as augmented and virtual reality (AR/VR) at the forefront of immersive data visualization and analysis.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Meet KAUST prospective student: Feng Liang

1 min read ·
Feng Liang is a computer science and engineering graduate who will join KAUST from the Southern University of Science and Technology (SUSTech), China. Feng will join the University this fall as a M.S. candidate and member of the KAUST Nanovisualization Research Group under the supervision of Professor Ivan Viola.

Dr. Ciril Bohak Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Dr. Ciril Bohak has joined VCC as a Postdoctoral researcher of Computer Science in October, 2020. Ciril Bohak is a Postdoctoral Researcher and Teaching Assistant in the Laboratory for Computer Graphics and Multimedia, Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His main research interests are Computer Graphics, Data Visualization, and Game Technology. His current research includes real-time medical and biological volumetric data visualization on the web, visualization of geodetic data (LiDAR and orthophoto) and high-energy

Yongliang Yang

Research Scientist, Computer Science
Yongliang Yang was a Research Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in Department of Computer Science at University of Bath in United Kingdom. Education and Early Career Yongliang received his Bachelor degree in Computer Science and Technology in 2004, from Tsinghua University. He continued to pursue his Master and Ph.D. degree in Graphics and Geometric Computing Group in Tsinghua, under the supervision of Prof. Shi-Min Hu. In 2009, he joined KAUST as a Postdoctoral Fellow working with Prof

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in
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Xingdi Zhang

Ph.D. Student, Computer Science
Visualization: Riemannian Geometry based observer relative vortex extraction; Computer vision: depth estimation, point cloud understanding. Computer graphics: discrete geometry, graphics engine, etc.

Wolfgang Heidrich

Professor, Computer Science
Professor Heidrich is apinoeer in the development of novel sensing and display technologies by combining computer graphics, machine vision, imaging, inverse methods, optics and perception.

Torsten Hadrich

Ph.D. Student, Computer Science
Torsten Hadrich is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Torsten Hadrich obtained his bachelor degree in Computer Science and Media from Stuttgart Media University in Germany in 2010. After that, he received his master degree in Information Engineering from the University of Konstanz in Germany in 2014. Torsten started his career life early in 2002. He was an IT Specialist for Application

Suhib Alsisan

Suhib Alsisan was a Master student in Computer Science Program at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, he is a Software Engineer at Facebook, Washington, United States. Education and Career He received his bachelor degree in Computer Engineering from University of Jordan, Jordan in 2009. He joined KAUST and earned his master degree in Computer Science in 2011. Education Profile M.S. Computer Science, King Abdullah university of Science and Technology (KAUST), Saudi Arabia, 2011 B.S., Computer Engineering, University of Jordan

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Ronell Sicat

Visualization Scientist, Core labs and Research Infrastructure
Ronell Sicat is a scientist with more than 10 years of experience in scientific research and software development. His main interests are scalable visualization and analysis of large-scale data (e.g., images, volumes, meshes), and immersive analytics that leverage augmented/virtual reality. He is passionate about helping domain scientists solve their problems using my wide-ranging experience in programming, image processing, segmentation, quantitative analysis, and computer graphics.

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Ondrej Strnad

Research Scientist, Computer Science
Ondrej Strnad is a Research Scientist currently working with Professor Markus Hadwiger in the High-Performance Visualization Group at KAUST. His research focuses on large-scale, out-of-core oil reservoir visualization. Ondrej joined KAUST in November 2019, initially becoming a member of Ivan Viola's Nanovisualization Research Group, where he worked on procedural modeling, generation, and visualization of highly populated, dense biological scenes.

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 IT Specialist with a background in quantum engineering and data science. He leverages analytical, multidisciplinary, and data-driven approaches to drive innovation and efficiency within the IT infrastructure. He excels at full-stack development, identifying complex problems' root causes and developing impactful long-term solutions within a broad area of competence.

Libo Huang

Ph.D. Student, Computer Science
Libo Huang is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Libo Huang obtained his bachelor degree in Geophysics from Peking University in China in 2015. After that, he joined KAUST and received his master degree in Earth Science and Engineering in 2016. Education Profile M. Sc., Earth Science and Engineering , King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2016 B. Sc

Juris Grigs

Ph.D. Student, Computer Science
Juris Grigs is a Computer Science Ph.D. student in the KAUST High-Performance Visualization Group (VCCVis) under the supervision of Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career Juris obtained his bachelor's degree in ​Physics from the University of Latvia in 2017. He joined KAUST as an Electrical Engineering student with the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group. Now,he is a Ph.D. student in the VCCVis Research Group. Research Interests Juris's research interests include ​data

Julio Rey Ramirez

Ph.D. Student, Computer Science
Julio Rey Ramirez is a Master student under the supervision of Prof. Wolfgang Heidrich at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Julio Rey Ramirez was an electronics engineer from National University of Colombia (UNAL). He worked as laboratory assistant in the lighting testing lab (LABE) at UNAL from 2015 to 2018. Julio Rey Ramirez joined KAUST as a visiting student in 2019, and worked in the programming of machine vision cameras and computational microscopy under the supervision of professor Wolfgang Heidrich

Jing Ren

Ph.D. Student, Computer Science
Jing Ren is a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jing Ren obtained a Bachelor’s Degree in Mathematics and Applied Mathematics from Zhejiang University in China. Later on in 2015, she received her M.Sc. degree in Mathematical and Computational Finance from the University of Oxford in United Kingdom. Scientific Interest Jing Ren is mainly interested in her research in Computer Graphics and Geometry Processing. Selected Publications One of the recent publication that Jing Ren co

Ibraheem Alhashim

Ibraheem Alhashim is a Remote Researcher working with Professor Peter Wonka. Education and Early Career Ibraheem Alhashim completed his B.Sc. with honors in Computer Science at the Portland State University in 2008, followed by his M.Sc. in Computer Science in 2011 under the supervision of Prof. Hao Zhang and his Ph.D. in Computer Science in 2016 under the supervision of Prof. Hao Zhang and Prof. Ghassan Hamarneh at Simon Fraser University in Canada. During both his Master and Ph.D., Alhashim was working at the GrUVi Lab at Simon Fraser University in Canada. Dr. Alhashim joined KAUST in 2017

Han Shao

Han Shao is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Han Shao obtained his bachelor degree in Theoretical and Applied Mechanics from The University of Science and Technology of China (USTC) in Hefei, China in 2014. After that, he joined KAUST and received his master degree in Mechanical Engineering in 2015. Research Interest Han is investigating the combustion phenomenon using CFD tools. For now

Feng Liang

Feng Liang is a former member of the Nanovisualization Research Group at KAUST lead by Professor Ivan Viola. Before joining KAUST, Feng obtained a bachelor's degree in computer science and engineering from the Southern University of Science and Technology (SUSTech), China. Research Interests Feng's research interests include artificial intelligence and computer graphics.

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Donggang Jia

Ph.D. Student, Computer Science
Donggang Jia is a Ph.D. student in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Donggang has a Master's Degree in Artificial Intelligence from the University of Southampton, United Kingdom. Before that, he received his Bachelor's degree in Computer Science at the Beijing University of Posts and Telecommunications, China. Research Interest Donggang is focusing on computer graphics and visualization

Dawar Khan

Postdoctoral Research Fellow, Computer Science
Dawar Khan has been working as a Postdoctoral researcher with Professor Ivan Viola in the Nanovisualization Research Group, at Visual Computing Center, KAUST - King Abdullah University of Science and Technology, Saudi Arabia, since April 2022. Previously he worked as assistant professor at Interactive Media Design Lab, Nara Institute of Science and Technology (NAIST), Japan and at the University of Haripur, Pakistan. Education and Early Career He received the BS and MS degrees in computer science from the University of Malakand, Pakistan, in 2011 and 2014, respectively, and the PhD degree in

Daoming Liu

Postdoctoral Research Fellow, Computer Science
Daoming Liu is a Postdoctoral Research Fellow working with Professor Helmut Pottmann at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Daoming Liu obtained his Ph.D. degree from the University of Chinese Academy of Sciences (UCAS). During his Ph.D., he spent one year at TUDelft as a visiting doctoral student. Before joining VCC/KAUST, he worked at ShanghaiTech University as a Research Assistant Professor. Research Interest Daoming Liu’s research was focused on physical simulation, geometric computing and their

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral

Bolun Wang

Postdoctoral Research Fellow, Computer Science
Bolun Wang is a Postdoc at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and early career 2016.09-2022.01 Ph.D. in fundamental mathematics at Honors College & Key Laboratory of Mathematics, Information and Behavioral, Beihang University. Advisor: Prof. Zhiming Zheng and Xin Jiang 2019.02-2020.09 Visiting Student at Geometric Computing Lab, Courant Institute of Mathematical Sciences, New York University Major in Computer Graphics, Computational Geometry Advisor: Prof. Daniele Panozzo 2012.09-2016

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Alexandre A. Kouyoumdjian

Research Scientist, Computer Science
Dr. Alexandre A. Kouyoumdjian has joined the Nanovisualization Research Group as a Research Scientist working with Professor Ivan Viola. His main research interests are Visualization & Computer Graphics, Human-Computer Interaction & Virtual Reality, and Microarchitecture. Dr. Alexandre A. Kouyoumdjian has also worked in the industry as an R&D Consultant for a start-up that develops VR applications meant to ease chronic pain and a graphics programmer for a game studio. Education Profile Ph.D., Computer Science, Université Paris-Saclay,FRANCE, 2018 M.S., Computer Science (Bioinformatics and

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.

Abdalla Gaafar

Postdoctoral Research Fellow, Computer Science
Abdalla Gaafar is a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdalla obtained his Ph.D. in Computer Graphics at the University of Konstanz in Germany in 2019. He holds a Master of Science degree in Mobile and Personal Communications from King’s College London, and a Master of Business Administration degree from the University of Khartoum in Sudan. Research Interest Dr. Abdalla is mainly interested in Computer Graphics and Visual Computing areas
<> Environmental Statistics (45)

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.

Workshop on Computational Space-Time Statistics

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B1 L4 R4102

Workshop on Computational Space-Time Statistics

Uncertainty Quantification of Tsunami Models

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B1

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

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B1 L4 R4102

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

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B1 L4 R4102

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Professor Sun receives the Environment Early Investigator Award 2017

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

1 min read ·
Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Second year Ph.D. student to receive top statistics award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Statistics plot pollution to inform policy

1 min read ·
The spatial variation in different air pollution components helps identify possible targets for pollution control.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Expanding the scale of dangerous weather prediction

1 min read ·
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.

KAUST Ph.D. student receives environmetrics best poster award

1 min read ·
Ghulam Qadir, a third-year Ph.D. student in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received a best poster award from the Italian Environmetrics Society (GRASPA) and the International Environmetrics Society (TIES) at the GRASPA 2019 conference held from July 15 to 16 in Pescara, Italy.

Shaking up the landslide model

1 min read ·
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Meet KAUST prospective student: Cristian Felipe Jiménez Varón

1 min read ·
Cristian Felipe Jiménez Varón is an applied mathematics graduate who will join KAUST from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering fromUNAL .

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Getting to the bottom of deadly weather

1 min read ·
Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Professor Marc Genton receives the prestigious 2024 Don Owen Award

1 min read ·
KAUST Professor Marc Genton, a pioneer in environmental statistics, was awarded the prestigious 2024 Don Owen Award for his outstanding contributions to research, consultation, and service to the statistical community.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Wu Wang

Postdoctoral Research Fellow, Statistics
Wu Wang is a postdoctoral fellow at King Abdullah University of Science and Technology (KAUST) working with Professor Ying Sun in the environmental statistics research group. Education Wu Wang received his Ph.D. degree in June 2017 from Fudan University, China. He joined KAUST in September 2017 Wu Wang received his Bachelor's degree in 2008 from East China Normal University Research Interest Wu Wang' s research interests lie in spatial varying coefficients regression with application in particulate matter modeling. He is also interested in functional data analysis, especially functional linear

Stefano Castruccio

Postdoctoral Research Fellow, Statistics
Stephano Castruccio is currently working as an Assistant Professor at the University of Notre Dame (USA). Stefano worked as a Post Doc in KAUST, under Distinguished Professor Marc Genton . He did his PhD from University of Chicago, USA Research Interests Spatio-temporal modeling, climate model sensitivity, hierarchical modeling, monitoring air pollution

Sabrina Vettori

Ph.D. Student, Statistics
Sabrina Vettori was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the joint supervision of Prof. Marc Genton and Prof. Raphaël Huser. She successfully defended her Ph.D. thesis entitled " Models and Inference for Multivariate Spatial Extremes" and graduated in December 2017; see his PhD thesis here. Her PhD committee was composed of Professors Marc Genton (chair), Raphaël Huser (co-chair), Anthony Davison (external examiner from EPFL, Switzerland), Ying Sun and Slim Alouini. After her PhD, Sabrina co-founded the successful KAUST start-up

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Luigi Lombardo

Postdoctoral Research Fellow, Statistics
Dr. Luigi Lombardo joined the Extreme Statistics Research Group and worked as Postdoctoral Research Fellow from April, 2016, to December, 2018, under the joined supervision of Prof. Raphaël Huser and Prof. Martin Mai. After his postdoc at KAUST, Luigi moved to ITC, University of Twente, Netherlands, where he embraced an academic career by becoming an Assistant Professor of Soil Science. See his personal website here. Education and early career Luigi Lombardo received his Ph.D. from Universita’ degli Studi di Palermo, Palermo, Italy in 2015. He joined KAUST as a postdoc in April 2016, and moved

Junho Lee

Postdoctoral Research Fellow, Statistics
Short Bio about yourself Junho Lee received both undergraduate and Master degrees in Statistics from Yonsei University, Seoul, Korea. After his Master degree and before beginning his Ph.D. study, Junho Lee had worked at Korea Credit Bureau as a researcher to develop the Credit Scoring system for Bank, Credit Card and Insurance industries in Korea. Before joining KAUST in August 2017, Junho Lee received his Ph.D. in Statistics from University of Wisconsin-Madison, USA. Here, at KAUST, Junho Lee is working at Environmental Statistics Group with Prof. Ying Sun. Education and Early Career Ph.D. in

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Huang Huang

Ph.D., Statistics
Career 2022-, Quantitative Researcher & Developer, Abu Dhabi Investment Authority (ADIA), UAE. 2019-2021, Research Scientist, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research statistical modeling of multivariate spatio-temporal data with large size Apply machine learning models to environmental data with comparisons to spatio-temporal statistical models Lead reading groups on spatial machine learning Collaborate with computer scientists in developing high-performance packages for large geostatistical inference Mentor students on their internship on research

Ghulam A. Qadir

Ph.D. Student, Statistics
Ghulam Qadir is a Ph.D. student in the Environmental Statistics group under the supervision of Prof. Ying Sun at the King Abdullah University of Science and Technology (KAUST). He believes that KAUST creates a perfect research-friendly environment where students are provided with excellent resources which allow them to turn their research creativity into valuable discoveries as well as make them ready for their future endeavors. Education and Early Career M.Sc. Statistics, Hindu College, University of Delhi, 2016 B.Sc. (Hons.) Statistics, Hindu College, University of Delhi, 2014 Research

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.

Carolina D. Euan Campos

Postdoctoral Research Fellow, Statistics
Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016. She joined KAUST as a Postdoctoral Research Fellow in October 2016. Research Interest High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes, Complex Data Visualization, Computational Statistics. Awards and Distinctions Sylvia Esterby Presentation Award

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> data mining (44)

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

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B9 L2 R2322

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.

Bio-Hackathon MENA 2023

Bio-Hackathon MENA 2023

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KAUST Hotel

Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

KAUST prospective student: Xiaochuan Gou

1 min read ·
Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

Xiaochuan Gou

Ph.D. Student, Computer Science
Xiaochuan Gou is a Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group (MINE) under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Xiaochuan obtained his master’s degree in Computer Science from National Chiao Tung University and a bachelor’s degree in Spatial Information and Digital Technology from Wuhan University, China, respectively. Research Interests Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shuai Zheng

Shuai Zheng obtained his Master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). First employment: Ph.D. student, University of Texas at Arlington​, 2013 Research Interests Shuai's research interests included Data mining, Cloud computing and High Performance Computing. Selected Publications ​Dan Williams, Shuai Zheng, Xiangliang Zhang, Hani Jamjoom, "TideWatch: Fingerprinting the Cyclicality of Big Data Workloads". Accepted by the

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Hongyan Bao

Ph.D. Student, Computer Science
Hongyan Bao is a Ph.D. student at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests ​​Hongyan's research interests include Data Mining. Selected Publications ​Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. Co-Embedding Attributed Networks. Accepted by The Twelfth International Conference on Web Search and Data Mining (WSDM 2019), February ​11-15, 2019, Melbourne, Australia. (Acceptance rate = 84/511 = 16%). Education Profile B

Guoda Chen

Guoda Chen obtained his M.S. degree in Computer Science under the supervision of Prof. David Keyes (HiCMA Research Group) and Prof. Panagiotis Kalnis (Cloud Research Group) at King Abdullah University of Science and Technology (KAUST). Research Interest Guoda's research interests included Data Management and Data Mining.

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Ehab Abdelhamid

Ph.D. Student, Computer Science
Ehab Abdelhamid is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Ehab's research interests include Data mining over very large datasets and Information Retrieval. Currently working on pattern mining in large graphs; The approach currently developed eliminates unnecessary iterations that could not be avoided in previous related work. As a result, improved performance in large graphs. Selected Publications ​Ehab A. Mohamed , Ahmed R.and Samhaa R. , 2009,

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Basmah Altaf

Basmah Altaf obtained her Ph.D. degree in Computer Science under the supervision of Prof. Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). Research Interests​​ Basmah's research interests include Recommender systems and Data mining​. Education Profile ​B. E., Computer & Information Systems, N.E.D. University of Engineering and Technology, Karachi, Pakistan, 2010. M. S., Computer Science, KAUST, 2013. Awards and Distinctions ​'Gold Medal' for securing '1st Position' among 140 students in Bachelor

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim
<> High Performance Computing (41) <> spatio-temporal statistics (36)

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.
KAUST-CEMSE-Public-colloquium-Ying-Sun-Spatio-Temporal-Statistics-In-Geo-Environmental-Data-Science

Spatio-Temporal Statistics in Geo-Environmental Data Science

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B9, L2, R2322

In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on geo-environmental data science. Our work is primarily centered around the development and application of sophisticated statistical models that improve the understanding and management of environmental data characterized by their spatial and temporal variability. My group has made significant advances in developing better spatio-temporal models that effectively capture the complexities inherent in environmental datasets, as well as developing innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase our research contributions motivated by environmental applications, including multivariate time series visualization and clustering, panel data analysis for functional and spatial data, and statistical process monitoring.

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

New paper accepted in JABES

1 min read ·
New accepted paper: Castro-Camilo, D., Huser, R., and Rue, H. (2019+), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics, to appear [ PDF preprint]

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

Meet KAUST Prospective Student: Amin Wu

1 min read ·
Amin Wu is a 24-year-old graduate who will join KAUST from the Communication University of China. Wu will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Environmental Statistics Research Group under the supervision of Professor Ying Sun. Wu wants to be a teacher or researcher and use her expertise and knowledge to make the world a better place.

Meet KAUST prospective student: Xiaotian Jin

1 min read ·
Xiaotian Jin is a graduate of Wenzhou-Kean University in China (a campus of Kean University in the USA). Xiaotian will join KAUST this fall as a M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin.

Meet KAUST new student: Qilong Pan

1 min read ·
Qilong Pan is a statistics graduate who joined KAUST in June 2021 from the Wuhan University of Technology, China. Pan is a M.S./Ph.D. student and member of the Environmental Statistics research group under the supervision of Professor Ying Sun.

Zipei Geng

Ph.D. Student, Statistics

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Ying Sun

Associate Professor, Statistics
KAUST Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Xiaotian Jin

Ph.D. Student, Statistics
Xiaotian Jin is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin. Before joining KAUST, Xiaotian obtained a bachelor's degree in computer science from Wenzhou-Kean University, China. Research Interests Xiaotian's research interests include statistical modeling and Spatio-temporal statistics.

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Soumya Das

Ph.D. Student, Statistics
Soumya Das is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton. Education and Early Career M.sc. (2015-17) from Indian Institute of Technology Kanpur, Uttar Pradesh, India B.Sc. (2012-15) from Ramakrishna Mission Residential College (Autonomous), Narendrapur, Kolkata, West Bengal, India Research Interests Cyclostationary processes, Non-parametric analysis, Non-stationary models, Skewed distributions, Spatio-temporal statistics, Threshold models. Awards and Distinctions The General Proficiency Medal for

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Mary Lai O. Salvana

Ph.D. Student, Statistics
Mary Salvaña is a PhD student in the Spatio-Temporal Statistics and Data Science group under the supervision of Prof. Marc Genton at the King Abdullah University of Science and Technology (KAUST). Education and Early Career MS Applied Mathematics graduate from the Ateneo de Manila University (Philippines) Research Interest Spatio-Temporal Statistics, Wind Energy Modelling, Renewable Energy Modelling.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Daniela Cisneros

Ph.D. Student, Statistics
Daniela Cisneros was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Daniela successfully defended her PhD thesis entitled " Extreme-Value Models and Graphical Methods for Spatial Wildfire Risk Assessment" on September 11th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Jorge Mateu (external examiner from Universitat Jaume I, Castillón, Spain), Hernando Ombao, and Matteo Parsani. For her next career steps, Daniela will return to Mexico where

David Bolin

Associate Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.

Amanda Lenzi

Postdoctoral Research Fellow, Statistics
Amanda Lenzi is currently working as a Postdoctoral fellow under Mihai Anitescu, in Argonne National Laboratory, Chicago USA Amanda Lenzi was a Post-Doctoral fellow in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc G. Genton in his research group. Education and Early Carreer Amanda Lenzi completed her Ph.D. at the Technical University of Denmark (DTU) in 2017. She also holds a B.S. and an M.S. degree in Statistics from the University of Campinas (UNICAMP), Brazil. She joined KAUST in September 2017. Research Interest Her

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards
<> communications (35)

Network Engineering as Distinct Academic Discipline Beyond the TEE Movement

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B1 B3 R3119

Abstract: Network Engineering as Distinct Academic Discipline Beyond the TEE Movement Network Engineering is now recognized as distinct education discipline in the USA and in many other countries. This development came about with ABET’s1 2015 (current) Criteria for Electrical, Computer, Communications, Telecommunication(s) and Similarly Named Engineering Programs. Few academic programs focused on this area of study in the past. More programs are expected to emerge now with progress in this field and with ABET recognition of it. Such programs have a great potential to prepare a new generation

Visible light communication

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B9 H1

The wireless communication technology has been evolved from 1G to 5G networks. The next 6G era will bring about higher demands for bandwidth and speed. Visible light communication (VLC) shows great potential in providing high speed and secure communication link by utilizing the unregulated visible light spectrum to carry the data. In this seminar, the overview of VLC will be presented first. The devices and components developed for VLC network will then be reviewed and discussed in details. I will also briefly talk about the application of underwater communication application by using visible light at the end of the seminar.

Gbit/s Visible Light Communications

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KAUST

Visible light communication (VLC or LiFi) has been a topic of intense research after the idea was proposed in 2011. To date, a data rate of multiple 100s Mbps has been demonstrated using LED as light source. At KAUST, we are developing the next generation of SSL lighting using visible laser diodes (LDs) and superluminescent diodes (SLDs). Laser diodes and SLDs do not suffer efficiency droop at high current densities. This allows for the design of lamps using a single, small footprint, light-emitting chip operating at high current densities. Using a single chip reduces system costs compared with LEDs because the system uses less material per chip, requires fewer chips, and employs simplified optics and a simplified heat-sink. The chip area required for LED technologies will be significantly reduced using LD/SLD-based solid-state lighting. This technology will also enable highly controllable beams in term of tunable throw distance, tunable color temperature and rendering index. Multiple Gbit/s VLC links have been demonstrated using LD/SLD as transmitters. In this talk, I will focus on the recent progress of visible diode LD/SLD-based lighting technology and high-speed transmitters and receivers for multiple-Gbps VLC and underwater wireless optical communication.

Communications, Computing, and Security for the Internet of Things Era and Beyond

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B1 L3 R3119

This talk will first discuss the communications, computation, and security challenges for the IoT era and motivate a systematic methodology to address them. The talk will then overview some relevant problems and their potential solutions. Future research direction will also be highlighted.

Bruce Lee Inspired Fluid Antenna Systems for 6G

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B1 L3 R3119

In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some early results on fluid antenna systems, which shows its potential for improving wireless communication performance.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Towards The Implementation of Reliable Hyperloop Communication Systems

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B1 L3 R3119

This work is geared toward the design and analysis of reliable Hyperloop communication systems. Hyperloops pose several challenges for the communication system for providing a reliable communication system while dealing with a complex structure, moving at tremendous speeds inside an evacuated tube. The adopted sealed steel-structured tube in the Hyperloop prevents signal penetration, isolating the inner world from the outside and creating a highly scattering environment for electromagnetic (EM) waves. Furthermore, the exceptionally high speed of the traveling pod results in severe Doppler shifts and frequent handovers, leading to increased transmission errors and delays. We propose a novel system configuration where wireless transmitters or access points (APs) are placed inside the tube to communicate with the moving receiver using optical fiber as a backhaul link.

Dr. Basem Shihada and Dr. Sajid Ahmed promoted to Senior Member of IEEE

1 min read ·
Dr. Basem Shihada and Dr. Sajid Ahmed have been elevated to the rank of Senior Member of IEEE. This is considered a significant honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

KAUST's Prof. Basem Shihada's work on Buffer Sizing for Multi-Hop Networks, granted US Patent 8,638,686

1 min read ·
Prof. Basem Shihada and his previous research scientist Kamran Jamshaid obtained a US Patent for their technology on optimizing buffer sizing for multi-hop networks.

Prof. Osama Amin Elevated To Senior Member Of IEEE

1 min read ·
Dr. Osama Amin has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

Prof. Alouini selected as an IEEE Communications Society Distinguished Lecturer for 2016 and 2017

1 min read ·
Congratulations to Prof. Alouini for being selected to give a Distinguished Lecture for 2016 and 2017 in IEEE Communications Society

No strings attached for underwater video system

1 min read ·
An underwater wireless optical communications system for streaming high-quality, live video.

A clearer future for underwater exploration

1 min read ·
An optical communication system could revolutionize underwater exploration and discovery.

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE – call 4 - 2018

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A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska-Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY.

Brighter blue lights for faster communication

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High-performance blue-light-emitting diodes could boost white-light, high-speed data transmission.

Lighting the way to the underwater Internet of things

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An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

Tiny, fast, accurate technology on the radar

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A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Drones deliver on internet connectivity

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Hovering airborne vehicles could connect smart sensors to the internet of things.

Patterned optical chips keep secrets perfectly safe

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Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.

Internet of underground things to reveal buried vistas

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Pinpoint mesh of smart underground objects could give real-time 3D readout of fossil fuel reserves.

A pure twist on optical data transfer

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The finding that light can be twisted very precisely may offer fresh options for communications infrastructure.

Angling for underwater WiFi

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Scuba divers could send sea life shots in real time using an aquatic internet service.

Divided attention could ease wireless congestion

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Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Full color LEDs cut down to size

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Tiny light-emitting devices that can create all the colors in the rainbow are essential for the next generation of phones and screens.

Airborne antennas to address electro-magnetic fields exposure concerns

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Movable airborne antennas receiving cellphone signals could reduce EMF exposure while offering higher data transmission speed and using less power.

Connect the internet of bodies

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A network of body monitoring devices connected using our own tissue could form the basis for the internet of bodies (IoB).

Professor Eltawil officially inducted as a Senior Member of the National Academy of Inventors

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Earlier this year, KAUST Professor of Electrical Engineering Ahmed Eltawil was elected as a Senior Member of the National Academy of Inventors (NAI). Eltawil was officially inducted as a Senior Member at the recent NAI 10th Annual Meeting held in Florida, U.S.

Fiber glow brings more go for underwater communications

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KAUST scientists have developed an optical-based wireless network system that has significant potential for faster underwater optical communications.

Yongqiang Zhang

Yongqiang Zhang is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST).

Tareq Al-Naffouri

Professor Al-Naffouri's research focuses on designing and analyzing wireless communication systems, particularly spectrum sharing, cognitive radio, energy efficiency and security within the context of advanced technologies like 5G.

Sebastian Celis

Sebastian Celis is currently an MS/Ph.D. student at Professor Khaled Nabil Salama's Sensors Lab, in the Electrical Engineering Department, CEMSE division, King Abdullah University of Science and Technology (KAUST). Research Interests Sebastian's research interests include the development of electromagnetic devices for mobile communications and for radar systems, Antennas, GPR, Radars, Distribution Systems, Smart Grids, and 5G. Education and Early Career Sebastian Celis received a B.Sc. degree in electrical and electronics engineering from Universidad de Los Andes, Bogota, Colombia, in 2018

Mohamed-Slim Alouini

Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering
KAUST is a fertile place for any researcher who wants to develop rigorous computational and engineering methods to solve real-world problems. Its unique support of science allows the most advanced scientific revolution.

Basem Shihada

Professor, Computer Science
Professor Shihada research philosophy is to bridge the gap between theory and practice in a working system. Having intensively worked in computer networks from both theoretical and systems aspects, Shihada has acquired both a solid mathematical background and an acute sense of applying these results to build practical systems.

Alam Zaib

Research Intern at King Abdullah University of Science and Technology (KAUST) ,Assistant Professor / COMSATS institute of information technology, Abbottabad.

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).
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Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Professor Tareq Al-Naffouri named Institute of Electrical and Electronics Engineers (IEEE) Fellow

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KAUST Professor Tareq Al-Naffouri has been named an IEEE Fellow, the organization’s highest distinction, in recognition of his contributions to adaptive signal processing and its applications in wireless communications.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Spectrum Management for Future Generations of Cellular Networks

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The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

Abla Kammoun receives IEEE Wireless Communication Letters Award

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Abla Kammoun, a research scientist based in the University's SRI - Center for Uncertainty Quantification Laboratory, recently received the Institute of Electrical and Electronics Engineers (IEEE) Wireless Communication Letters (WCL) top editor award. Kammoun's achievement is a significant honor, as every year no more than five editors are selected for this award for their significant contributions and exemplary services to the IEEE WCL. The goal of the IEEE WCL is the rapid dissemination of original, cutting-edge ideas and timely, significant contributions in the theory and applications of wireless communications.

Measurement Selection: A Random Matrix Theory Approach

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Khalil Elkhalil , Student Member, IEEE, Abla Kammoun, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, and Mohamed-Slim Alouini , Fellow, IEEE Abstract This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low

Plugging into a 6G future with users at the center

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For a communications revolution, 6G development needs more human-centric research.

Connecting the Unconnected at the KAUST 6G Summit

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The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

KAUST Distinguished Professor Mohamed-Slim Alouini honored with IEEE accolades

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Professor Mohamed-Slim Alouini has been awarded both the 2019 Institute of Electrical and Electronics Engineers (IEEE) Communication Society Communication Theory Technical Committee (CCTC) Technical Achievement Award, and the 2020 IEEE Vehicular Technology Society James Evans Avant Garde Award. The CEMSE Division founding faculty member received the awards for his contributions in the modeling, design, performance evaluation, and optimization of wireless communication systems and networks.

Developing the future of digital inclusivity

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The second edition of the KAUST 6G Summit on "Connecting the Unconnected" was recently held online from Tuesday, September 28 to Thursday, September 30, 2021. As part of “The Decade of Digital Inclusion,” a semester-long master class from the Marconi Society, the summit covered the latest research and technological advancements in 6G global connectivity.

Shaping a 6G future

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This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.

Redha M. Radaydeh

Research Scientist, Communication Theory Lab
From Sept. 2009 to Sept. 2012, Redha M. Radaydeh was a research fellow at KAUST and then an associate research scientist at TAMU Qatar. From Sept. 2012 to Aug. 2016, he worked at Alfaisal University as an associate professor of EE. Research Interests • Overlaid multi-tier HetNets. • Performance analysis of UA communications. • Resource sharing, distribution, and allocation techniques. • Interference management and mitigation schemes. • D2D and small-cell communications in 5G cellular networks. • Mixed RF and FSO communications. • Physical layer security. • Cooperative relaying systems. •

Nader Bouacida

Nader Bouacida obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST) in 2011. Currently, he is a Ph.D. student at the University of California - Davis. Master's Thesis title: " Towards Controlling Latency in Wireless Networks". Research Interests Nader's research interests included Wireless Full-duplex communications, buffer management and developing next-generation wireless network technologies. Education Profile M.Sc., Computer Science, King Abdullah University of Science and Technology

Nasir Saeed

Postdoctoral Research Fellow, Electrical and Computer Engineering
Postdoctoral Fellow at Communication Theory Lab, King Abdullah University of Science and Technology (KAUST) working with Professor Mohamed-Slim Alouini and Professor Tareq Y. Al-naffouri ,Associate Professor with the Department of Electrical and Communication Engineering, United Arab Emirates University (UAEU)

Muhammad Mehboob Fareed

Postdoctoral Research Fellow, Communication Theory Lab
Muhammad Mehboob Fareed is now a Technical Lead in Mircom, Toronoto, Canda. Selected Publications Book Chapters: M. Uysal and M. M. Fareed, “Cooperative Diversity Systems for Wireless Communication,” Chapter in Handbook on Information and Coding Theory, I. Woungang, S. Misra and S. C. Misra (Eds.), World Scientific, 2010. PhD Thesis: M. M. Fared, "Optimum Power Allocation for Cooperative Communications," University of Waterloo, Waterloo, Canada, Aug. 2009. Journal Publications: M. M. Fareed and M. Uysal, “On relay selection for decode-and-forward relaying,” IEEE Transactions on Wireless

Mohammed Ahmed

Postdoctoral Research Fellow (former), Information Science Lab
post-doctoral fellow at L'École de technologie supérieure (ETS) de Montréal (Mtl) where he started on February 2014. he works with Prof. Georges Kaddoum.

Mohamed Habib Loukil

MS Degree in the Communication Theory Lab (CTL) under the supervision of Professors Mohamed-Slim Alouini and Tareq Al-Naffouri at King Abdullah University of Science and Technology (KAUST).

Mohamed A. Gaafar

Mohamed A. Gaafar was a CTL lab member and graduated from KAUST. Then, he joined the Technical University of Berlin as a PhD student. Selected Publications Selected Conferences 1. M. Gaafar, O. Amin, W. Abediseid, and M.-S. Alouini, “Sharing the licensed spectrum of full-duplex systems using improper Gaussian signaling,” in Proc. IEEE Global Commun. Conf. (GLOBCOM), San Diego, USA, Dec. 2015, pp. 1–6. 
 2. M. Gaafar, M. G. Khafagy, O. Amin, and M.-S. Alouini, “Improper Gaussian signaling in full-duplex relay channels with residual self-interference,” in Proc. IEEE Int. Conf. Commun. (ICC)

Meiwei Kong

Postdoctoral Research Fellow, Photonics Laboratory
Meiwei Kong is a Postdoctoral Research Fellow at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Sciences & Engineering at King Abdullah University of Science and Technology. She received her Ph.D. degree in marine information science and engineering at the School of Ocean College of Zhejiang University in 2018. Research Interests Meiwei's current research interests include digital signal processing, indoor and underwater optical wireless communication system and network design, and wireless power transfer. Selected Publications Journal Papers: M. W. Kong, Y. F

Jose Manuel Gonzalez

Jose Manuel Gonzalez worked as a Post Doctorate Fellow at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group with Professor Atif Shamim at King Abdullah University of Science and Technology (KAUST) during (2012-2013). Currently, Jose is working as an Adjunct Professor, in the Electronic Technology Department​ at the University of the Basque Country UPV-EHU (Universidad del País Vasco-Euskal Herriko Unibertsitatea). Research Interests Jose' research interests include RF circuit and EM structure design for agile wireless communications, Advanced LTCC System-on

Jiao Wu

Postdoctoral Research Fellow, Electrical and Computer Engineering
I believe that KSUAT will help me a lot.

Itsikiantsoa Randrianantenaina

Itsikiantsoa Randrianantenaina received the Engineering Diploma degree from the Institut National des Postes et Telecommunications, Rabat, Morocco, in 2013, and the M.S. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, from June 2015- June 2019, where she earned the Ph.D. degree and Joined NEOM, Saudi Arabia, as a Research Engineer after graduation. Her research interests include full-duplex and NOMA systems, interference management, FogRAN architecture, stochastic geometry, and optimization. Education Profile Ph.D

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory

Hessa Al-Quwaiee

Introduction Hessa Al-Quwaiee's interest in engineering started young, but in the early 2000s when Al-Quwaiee was attending high school, the path to becoming a female engineer in Saudi Arabia was not a commonly taken one. Instead, Al-Quwaiee pursued computer engineering at Prince Mohammed University in Al-Khobar. Education and Early Career One year into her bachelor's degree, a small announcement printed in a local newspaper that was keenly spotted by her sister called for students to apply for a scholarship at a yet-to-be-built university. Al-Quwaiee applied and was accepted, and her path to

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.
Hakim

Hakim Jemaa

KAUST is an environment that promotes competitivity and encourages personal growth in a rich multicultural community.

Hadi Sarieddeen

postdoctoral fellow in the Communication Theory Lab (CTL) and the Information Science Lab (ISL) at KAUST, working under the supervision of Distinguished Prof. Mohamed-Slim Alouini and Prof. Tareq Al-Naffouri.

Giovanni Chisci

Visiting Scholar, Communication Theory Lab
Giovanni Chisci works in the Department of Engineering, University of Ferrara, Ferrara, Italy. Selected Publications Journal Publications L. Mucchi, L. S. Ronga, G. Chisci, "Noise-Loop Multiple Access," IEEE Transsction on Vehicular Technology , vol. 65, no. 10, pp. 8255-8266, Oct. 2016. G. Chisci, H. ElSawy, A. Conti, M.-S. Alouini, M. Z. Win, "Spatiotemporal Modeling for Uncoordinated Massive Wireless Networks," Under review in IEEE/ACM Transaction on Networking. 
 G. Chisci, A. Conti, L. Mucchi, M. Z. Win, "Intrinsic Secrecy in Inhomogeneous Stochastic Networks," To appear in IEEE/ACM

Ganghui Lin

Ganghui is an MS student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ganghui obtained his bachelor’s degree in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2022. He was a visiting student at CTL (July 2021 – July 2022) before joining KAUST. Research Interest Ganghui is focusing on the area of the next generation of wireless communication, the Internet of Things (IoT), and deep learning. Honors and

Ferdaous Tarhouni

Ferdaous Tarhouni is an MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Ferdaous has studied mathematics and physics at the Preparatory Institute for Engineering Studies of Tunis and has been ranked among the top 2% in the national engineering school exam in 2020. This enabled her to enter the Ecole Polytechnique de Tunisie (EPT), one of the most prestigious engineering schools in Tunisia where she received multidisciplinary training. In her final year, she specialized in

Ayed M. Alrashdi

Ph.D. Student (former), Electrical and Computer Engineering
Ph.D. degree in electrical and computer engineering from the King Abdullah University of Science and Technology (KAUST) working with Professor Tareq Al-Naffouri in the Information System Lab (ISL),Assistant Professor with the Electrical Engineering Department at University of Ha'il

Amen Memmi

Amen Memmi was a Visiting B.S. Student in the CTL. Selected Publications Amen Memmi, Zouheir Rezki and Mohamed-Slim Alouini , "Power Control for D2D Underlay Cellular Networks with Imperfect CSI " Education 2012-2015: Polyvalent engineer majoring in Signals and Systems (SISY) at Ecole Polytechnique de Tunisie (EPT), Tunis, Tunisia Awards 2012-2015 : EPT Scholarship, Ecole Polytechnique de Tunisie, Tunisia Education Profile 2012-2015: Polyvalent engineer majoring in Signals and Systems (SISY) at Ecole Polytechnique de Tunisie (EPT), Tunis, Tunisia

Ali Chelli

Postdoctoral Research Fellow, Communication Theory Lab
Ali Chelli is currently a Post doctoral Research Fellow at Norwegian University of Science and Technology, Trondheim , Norway. Selected Publications Book Chapters: A. Chelli and M. Pätzold, “A non-stationary MIMO vehicle-to-vehicle channel model derived from the geometrical T-junction model,” Chapter in Vehicular Technologies: Increasing Connectivity, Edited by: Miguel Almeida, INTECH, Apr. 2011. A. Chelli and M. Pätzold, “The impact of fixed and moving scatterers on the statistics of MIMO vehicle-to-vehicle channels,” Chapter in Radio Communications, A. Bazzi (ed.), INTECH, Apr. 2010. Ph.D

Ahmed Eltawil

Professor Eltawil is an expert in wireless communication system design with an established history of university-industry collaboration. He continues to act as an adviser in developing wireless systems for leading international companies.

Abla Kammoun

Senior Research Scientist, Electrical and Computer Engineering
Abla Kammoun is a research scientist in the Communication Theory Lab (CTL) at King Abdullah University of Science and Technology (KAUST) led by Professor Mohamed-Slim Alouini. Education and Early Career Abla Kammoun got her Bachelor of Engineering from Tunisia Polytechnic school in 2005. In 2006 and 2010 respectively, she received her Master and PhD degrees from Télécom Paris Tech [then Ecole Nationale Supérieure des Télécommunications (ENST)] in France. Before joining KAUST, she worked as a Postdoctoral Fellow at Télécom Paris Tech then at Large Networks and Systems Group (LANEAS) in Supélec
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Additively Manufactured Conformal Microwave Sensors for Applications in Oil Industry

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Modern industries are adapting smart ways of monitoring their processes to ensure smooth operations. Sensors capable of early detection of a problem are becoming the norm in industrial processes. This is key to the development of the “Internet of Things” (IoT), in which billions of interconnected devices will work together to make smart decisions. Sensors that can detect and communicate the process information are essential ingredients of any IoT-enabled network. Since billions of such sensor nodes will be required in the future, the low cost will be an important feature for these devices. Consistent with the above-mentioned trends, the oil industry is also adapting smart monitoring and actuation mechanisms for its day-to-day operations. This thesis is focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells and also help in the safe transport of oil products from the wells to the refineries.

Wireless Powered Communications in the Era of 6G

Ioannis Krikidis, Associate Professor, Electrical and Computer Engineering, University of Cyprus

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We discuss the principles of WPC and we highlight its main network architectures as well as the fundamental trade-off between information and energy transfer. By following a bottom-up cross-layer approach, several examples, that deal with the fundamentals of WPC as well as its integration in modern communication systems, are presented. Specifically, we deal with circuit models for WPT, information-theoretic limits, signal processing aspects and waveform design, and system-level analysis by using stochastic geometry tools. Future research directions and challenges are also pointed out.

Scheduling tasks in energy harvesting IoT

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Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.

Prof. Andrea Fratalocchi receives appreciation from Nature Publications as one of its best reviewers

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Prof. Andrea Fratalocchi received a letter of appreciation from the Editor-in-Chief of Nature for serving as a reviewer.

Klemens Katterbauer (PhD in Electrical Engineering) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper contest

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Klemens Katterbauer (Ph.D. candidate under supervision Andrea Fratalocchi) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper Contest.

Ms Student wins the First Phase of the 10th Annual International Microelectronics Olympiad of Armenia

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Abdullah Almansouri, a MS student in the electro-physics track within the Electrical Engineering program, won the first phase of the 10th Annual International Microelectronics Olympiad of Armenia.

Prof. Osama Amin Elevated To Senior Member Of IEEE

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Dr. Osama Amin has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

Cancer screening advance: single point genetic mutations made visible by new optical chip

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The collaboration of the teams of professors Enzo Di Fabrizio and Andrea Fratalocchi at the King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, has led to the development of a new device that enables the detection of mutations down to a single amino acid.

Muhammad Akram Karimi secures 3rd prize Best Paper Award at IMS - world's most prestigious microwaves conference

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Karimi's work on low cost and zero weight water-cut sensors stood out for its quality and distinctive application-direct approach. Successfully, he proved cost reductions of 100 times lower and dropping weight to 1,000 times below than the ones currently available in the market.

CEMSE student gets Best Paper Award at 2DFDU2016

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CEMSE student Chun-Ho Lin won the Best Paper Award at the 2nd International Symposium on Devices and Application of Two-dimensional Materials (2016), held in Shanghai on June 20, 2016.

Ph.D. student Muhammad Akram Karimimi among Best Paper Award finalists

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Ph.D. student Muhammad Akram Karimimi from the research team of Atif Shamim, Assistant Professor of Electrical Engineering at KAUST and Director of IMPACT Laboratory, was one of the finalists in the Student Paper Competition at the International Microwave Symposium held in San Francisco, California, in May 2016.

CEMSE Student Altynay Kaidarova appointed member of the Next Generation Magneticans Advisory Board

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Altynay Kaidarova, a CEMSE Master student in the Electrical Engineering Program, under the supervision of Prof. Jurgen Kosel, has recently been elected as a member of the Next Generation Magneticans Advisory Board.

CEMSE Students: Muhammad Akram Karimi

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Muhammad Akram Karimi describes himself as an inquisitive-minded researcher who loves to solve industrial challenges through science and technology. Karimi is a third-year Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab under the supervision of Professor Atif Shamim. His current research focus is on the design of low-cost and printed microwave sensors for various industrial applications, with a prime focus on the oil industry.

Alumni Focus: Jhonathan Prieto Rojas, M.S. 2010, Ph.D. 2014 - electrical engineering

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Jhonathan Prieto Rojas joined KAUST in 2009 from the National University of Colombia in Bogota and completed both his master's degree (2010) and Ph.D. (2014) in electrical engineering at the University under the supervision of Professor Muhammad Mustafa Hussain. Prieto Rojas spent six years at KAUST before leaving the University in August of 2015 for his current role as assistant professor in the electrical engineering department at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran.

Student Focus: Muhammad Akram Karimi

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Muhammad Akram Karimi describes himself as an inquisitive-minded researcher who loves to solve industrial challenges through science and technology. Karimi is a third year Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab under the supervision of Professor Atif Shamim. His current research focus is on the design of low-cost and printed microwave sensors for various industrial applications, with a prime focus on the oil industry.

A breath of fresh information to help diagnosis

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A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Andrea Fratalocchi Elected OSA Fellow for Pioneering Innovations

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Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently elected as a Fellow Member of The Optical Society of America (OSA) at the Society’s Board of Directors meeting in September.

KAUST Associate Professor Andrea Fratalocchi elected Fellow of the Optical Society

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Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering division, was recently elected as a Fellow of the Optical Society (OSA) at the society's Board of Directors meeting in September.

Saudi researcher’s magnetic skin research garners widespread attention

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Imagine if there was a revolutionary wearable technology that could enable the user to open doors or operate machinery with a simple wave of their hand or a mere blink of their eye? What people might not realize is that this contact-free human-machine technology already exists—and it has been developed right here in a laboratory at KAUST.

KAUST New Faculty: Yating Wan, Assistant Professor of Electrical and Computer Engineering

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Yating Wan, assistant professor of electrical and computer engineering, joins KAUST as a member of the KAUST Photonics Laboratory from her most recent position as a postdoctoral research associate at the University of California, Santa Barbara (UCSB), U.S.
Yating Wan

Yating Wan

Dr. Yating Wan, an assistant professor of electrical and computer engineering at KAUST, excels in silicon photonics, with special emphasis on the integration of on-chip light sources.

Thang Nguyen Quang

Thang Nguyen has recently joined the Sensors group as an Ms/PhD student. Education and early career Thang Nguyen received his BE in Electronics & Communications Engineering (honor program) from HCMUT in 2017. He had served as a research assistant at RFIC Lab, HCMUT for a year before joining KAUST. Research Interests Thang is interested in working in RF energy harvesting and Wireless Sensors Networks. Areas of Expertise and Research Interests RF energy harvesting Wireless Sensor Networks Cognitive Radios Education Profile BE in Electronics & Communications Engineering (honor program), 2017

Nour Kouzayha

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
Research Scientist at King Abdullah University of Science and Technology.

Muhammad Akram Karimi

Muhammad Akram Karimi obtained his Ph.D. degree in Electrical Engineering under the supervision of Professor Atif Shamim at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Research Interest Muhammad's research interests included the Design of Cost-Effective and robust Fluidic Sensors to characterize fluids (esp. Crude Oil) and Energy Harvesting. Selected Publications ​Journal : M. A. Karimi, M Arsalan and A. Shamim, "Low cost and pipe conformable microwave-based water cut sensor", IEEE Sensors

Jose Ilton De Oliveira Filho

Postdoctoral Research Fellow, Electrical and Computer Engineering
José Ilton is currently a Postdoctoral Fellow in the Electrical and Computer Engineering program, at King Abdullah University of Science and Technology (KAUST). José is focused on instrumentation, prototyping, and communication theory. Education and Early Career José Ilton did his bachelor’s in electrical engineering on the track of Power Systems at Federal University of Piaui with 1.5-year exchange period at Budapest University of Technology and Economics, in 2015. At King Abdullah University of Science and Technology (KAUST) he did his master’s in electrical engineering on the topic of

Juan Manuel Marin Mosquera

Early stage researcher focused on laying the foundations for the emerging field of Integrated Sensing and Communications (ISAC). His work aims to incorporate smart sensing solutions leveraging machine learning-aided Distributed Fiber Optic Sensing (DFOS) into the next generation of fiber-based telecommunication networks, as well as their integration into the realm of the Internet of Things (IoT).

Jhonathan Prieto Rojas

Jhonathan Prieto Rojas joined KAUST in 2009 from the National University of Colombia in Bogota and completed both his master's degree (2010) and Ph.D. (2014) in electrical engineering at the University under the supervision of Professor Muhammad Mustafa Hussain. Prieto Rojas spent six years at KAUST before leaving the University in August of 2015 for his current role as assistant professor in the electrical engineering department at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran. Jhonathan possesses natural talent of process integration. His extensive experience includes

Galo Torres Sevilla

Galo Torres Sevilla obtained both, his MS Degree and Phd Degree in Electrical Engineering in the Computer, Electrical and Mathematical Science & Engineering division of King Abdullah University of Science and Technology. He was a member of the Futuristic Electronics and Integrated Nanotechnology Lab and his Thesis was supervised by Prof. Muhammad Mustafa Hussain. Research Interest Galo's research interests include Flex-Si platform to demonstrate energy harvesting devices and nano-structured transistors for high-performance logic operation. Awards and Distinctions Graduate Fellowship, KAUST

Esraa Fakeih

Esraa Fakeih is an MS/PhD student in Prof. Khaled Salama’s Sensors group. She is studying electro-physics and is currently working on underwater mechanical rectifiers. Education and early career Esraa obtained her undergraduate degree in Electrical and Computer Engineering from Effat University in 2018. During her studies she undertook a 6-month internship in KAUST where she worked on radio frequency energy harvesting systems. Research Interests Esraa’s research interests include biomedical electronics and energy harvesting. Publications A smart home appliances control system based on digital

Chun-Ho Lin

Chun-Ho Lin obtained his Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jr-Hau He in 2018. Lin completed his master’s degree in the Department of Electrical Engineering from the National Taiwan University and began his Ph.D. studies at KAUST in 2014. Research Interest Chun-Ho Lin's research interests include Perovskites, Nanophotonics, Flexible Electronics, Paper device, and 2D Materials.

Andrea Fratalocchi

Professor Fratalocchi is a pioneer in complex systems and sustainable technologies, focused on addressing critical global challenges. His groundbreaking research has led to innovative solutions in areas such as energy harvesting, clean water production and smart materials. As a founder of the ECE program at KAUST and co-founder of Pixeltra, Professor Fratalocchi is a proven leader and entrepreneur, successfully translating his research into real-world impact.

Ahmed Sultan Salem

Instructional Professor, Electrical and Computer Engineering
Instructional Professor of Electrical and Computer Engineering Ahmed K. Sultan has extensive experience teaching at graduate and undergraduate levels in topics such as wireless communications and mathematics for signal processing and digital communications.

Adnan Ahmad Khan

Adnan Ahmad Khan obtained his Master's degree in Electrical Engineering under the supervision of Professor Atif Shamim at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Currently, Adnan is working as a Control Systems Engineer at Saudi Aramco, Saudi Arabia. Research Interests Adnan's research interests included Rectennas, Energy Harvesting using RF Energy and Inkjet printing of Electronics. Education Profile B.Eng. 2011, Electrical and Electronic Engineering, Nanyang Technological University

Abdullah Almansouri

Almansouri is a PhD student in the field of wireless powering and wireless sensing. Education and early career Almansouri received a B.Sc. degree (with honors) in Electronics and Communication Engineering from the Royal Melbourne Institute of Technology University (RMIT), Melbourne, VIC, Australia, in 2013, and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in 2016. Almansouri completed his Ph.D. at KAUST successfully. Research Interests Almansouri’s research interests revolve around wireless sensors, including wireless
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Data assimilation and optimal control in the context of UAV-based flash flood monitoring

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Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. The emergence of Unmanned Aerial Vehicles (UAVs) gives the possibility to monitor these events over large geographical areas. In this talk, we focus on the problem of trajectory planning for a swarm of unmanned aerial vehicles sensing flooding conditions.

CEMSE Welcomes new Professor: Jeff Shamma

1 min read ·
The CEMSE Division has recently welcomed a new Professor of Electrical Engineering, Dr. Jeff Shamma. Prof. Shamma joins from the Georgia Institute of Technology (Georgia Tech).

Professor Meriem Laleg's research on modeling contaminant transport published in SIAM Journal on Scientific Computing

1 min read ·
A new algorithm is proposed to estimate the average velocity, dispersion coefficient, and differentiation order of a space-fractional advection-dispersion equation used for modeling contaminant transport in porous media. This allows for the characterization of the medium and the determination of the contaminant source. The algorithm is efficient, robust and fast.

Professor Meriem Laleg's research on brain response models published in Journal of Neuroscience Methods

1 min read ·
A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements.

Professor Meriem Laleg's research on membrane distillation modeling for desalination published in Desalination

1 min read ·
An accurate mathematical model is proposed to describe an emerging desalination technology called direct contact membrane distillation system. The mathematical model is important for designing efficient control and monitoring strategies, a crucial step to facilitate the commercialization of this technology.

Jeff Shamma elected IFAC fellow 2014-17

1 min read ·
The award is an international distinction given to scientists for their contributions in the fields of interest of IFAC under the form of technical publications, patents, control solutions, products, software, and leadership in research, development, and education.

Team KAUST places 3rd at the Mohamed Bin Zayed International Robotics Challenge

1 min read ·
A team from the King Abdullah University of Science and Technology (KAUST) competed in the inaugural Mohamed Bin Zayed International Robotics Challenge (MBZIRC), held in Abu Dhabi, UAE, March 2017, and took a 3rd place finish in the ground robotics challenge.

KAUST hosts International Graduate School on Control event

1 min read ·
KAUST recently acted as a host campus for the European Embedded Control Institute's International Graduate School on Control (IGSC). The IGSC is an annual series of 27 one-week graduate modules focusing on different topics of networked and embedded control and is taught to eligible attendees at different locations worldwide. The series is co-sponsored by the Institute of Electrical and Electronics Engineers Control Systems Society and the International Federation of Automatic Control.

CEMSE New Faculty: Eric Feron, Professor of Aerospace Software Engineering

1 min read ·
Eric Feron is a professor of aerospace software engineering who recently joined the KAUST CEMSE Division from the Georgia Institute of Technology.

Faculty Focus: Taous-Meriem Laleg-Kirati

1 min read ·
-By A. Ortega, KAUST News KAUST Associate Professor of Electrical Engineering Taous-Meriem Laleg-Kirati joined the University in 2011 after working as a researcher at INRIA in France. Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group at KAUST, and her interests lie in control theory, system modeling and signal applications. She investigates multiple engineering disciplines and develops mathematical models and algorithms that control processes relying on real-time feedback. "My research goals [are the development of] effective estimation methods and algorithms to

An Airline observatory created by Passengers for Passengers

1 min read ·
The COVID-19 pandemic has generated a significant worldwide disruption to flight operations, affecting the lives of millions of passengers.

Usman Amin Fiaz

Areas of Expertise and Research Interests Robotics Intelligent Systems Design Multi-Agent Control Systems Aerial Grasping Self-Assembly & Self-Reconfigurable Systems ​ Education Profile PhD in Electrical Engineering, University of Maryland, College Park (UMD), United States of America (current) MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) BS in Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan (2015)

Sarah A. Toonsi

Areas of Expertise and Research Interests Robotics Control Systems Programming Education Profile ​BS in Electrical and Computer Engineering, Effat University, Saudi Arabia (2017) ​

Noureddine Toumi

Areas of Expertise and Research Interests Robotics Control Multi-agent systems Cooperative aerial transportation Education Profile BS ​Engineering from Tunisia Polytechnic School, Major: signal processing,systems conception and control.

Nawaf Alotaibi

Education 2020.08 - 2022.05 M.S. Student in Mechanical Engineering, KAUST, KSA 2016.08 - 2020.05 B.S. in Mechanical Engineering, Georgia Institute of Technology, USA Professional Profile 2019.06 - 2019.08 Research Intern, RISC Lab, KAUST, KSA Areas of Expertise and Research Interests ​Robotics Autonomous Vehicles Machine learning Vision

Mohammed M. Alrashed

Mohammed M. Alrashed research interests are in ​Systems and Control with robotic applications and Mathematical Theory of Rational Behavior. Master Thesis Abstract The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. The lack of understanding of panic stampedes still causes hundreds of fatalities each year, not to mention the scarce methodical studies of panic behavior capable of envisaging such crowd dynamics. Under those circumstances, there are thousands of

Mohammed Abdullah Algarni

Areas of Expertise and Research Interests Unmanned Aerial Vehicle (UAV) Robotics Autonomy Aerial Landing Education Profile MS in Mechanical Engineering, King Abdullah University for Science and Technology (KAUST), Saudi Arabia (2014​​) ​BS in Mechanical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia (2009)

Mohamed A. Mabrok

Postdoctoral Research Fellow, Electrical and Computer Engineering
From 2015 till 2017 Mohamed A. Mabrok was a research associate at Robotics, Intelligent Systems & Control (RISC) research group at King Abdullah University of Science and Technology (KAUST). Currently, he is an Assistant Professor at the Faculty of Engineering at the Australian College of Kuwait. He obtained his Ph.D. in the field of systems and control in 2013 from the University of New South Wales at Australian Defence Force Academy, Canberra, Australia. Mohamed also holds a bachelor degree in applied mathematics and a master degree in applied mathematics/quantum physics. Mohamed's research

Khaled Abu-Jbara

Khaled Abu-Jbara was a Ph. D. student in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Khaled Abu Jbara received his bachelor degree in Electrical Engineering, with Distinction, First Class Honors, from Kuwait University (KUNIV), Kuwait in August 2011. He worked as an Administrative Engineer and Teacher Assistant at Kuwait University, Electrical Engineering Department. After that, he joined King Abdullah University of Science and Technology (KAUST) as a

Khalid Alzuhair

M.S. Degree, Electrical and Computer Engineering, KAUST (King Abdullah University of Science and Technology).

Jiming Jiang

Areas of Expertise and Research Interests Circuit and System, Robotics, Wireless Sensor Network Education Profile PhD in Electronics, Cambridge University, United Kingdom (2010) MS in Analog and Digital Integrated Circuit Design, Imperial College, United Kingdom (2004​) BS in Electronic Engineering, Beijing University of Technology, China (2003)​

Jie Ma

MS/PhD student Electrical and Computer Engineering under the supervision of Prof. Tareq Al-Naffouri at King Abdullah University of Science and Technology (KAUST),

Hiroyuki Kuwahara

Hiroyuki Kuwahara got his Ph.D. degree in Computer Science from the University of Utah in 2008. He was then a junior researcher at Microsoft Research - Trento during 2007 and 2009. During 2009 and 2012, he was a Ray and Stephanie Lane Fellow at the School of Computer Science at Carnegie Mellon University. Since 2012, he joined the SFB group as a Research Scientist. Research Interests ​Hiroyuki's area of research is broadly in computational systems and synthetic biology. His main research focus is on the theoretical understanding of how various uncertainties contribute to the relation between

Haya Alsharif

Areas of Expertise and Research Interests ​​Control Systems Computer Vision Machine Learning Robotics Education Profile ​​BS in Electrical and Computer Engineering, King Abdulaziz University (KAU), Saudi Arabia (2016)

Hassan Jaleel

Postdoctoral Research Fellow, Electrical and Computer Engineering
Areas of Expertise and Research Interests ​Distributed control of multi agent systems using game theory and algebraic graph theory Energy aware algorithms for multi agent systems Cyber physical systems Stochastic geometry Coverage problems for sensor networks Education Profile PhD in Electrical & Computer Engineering, Georgia Tech (2013) MS in Electrical & Computer Engineering, Georgia Tech (2011) BSc in Electrical Engineering, UET (2007)

Gokul Krishnan S

PhD student at KAUST who works in grid design and control using HIL and RCP systems like RTDS, Opal-RT, Speedgoat, Typhoon and dSPACE.

Gilberto Mendoza Chavez

Areas of Expertise and Research Interests Robotics Control Systems Distributed Systems Education Profile Masters in Electrical Engineering, King Abdullah University of Science & Technology, KSA (2016)​ ​Bachelors in Mechatronics Engineering, Monterrey Institute of Technology and Higher Education, Mexico (2014)

Fatema Alhani

Education 2020.08 - Present M.S. D. Student in Electrical Engineering, King Abdullah University of Science and Technology, KSA 2016.07 - 2020.05 B.S. in Electrical and Computer Engineering, North Carolina State University, USA

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Bacem Ben Nasser

Bacem Ben Nasser is a postdoctoral researcher fellow in the Electrical Engineering department within the CEMSE division. He obtained his Ph.D. degree in Sciences and Technology-Mathematics from the University of Sfax, Faculty of Sciences of Sfax, Tunisia, where he worked in Lyapunov and control theories, nonlinear integral inequalities and time scale calculus. In January 2017, he was affiliated with the University of Kairouan, Tunisia, at the Higher Institute of Applied Sciences and Technology of Kairouan as assistant professor of applied mathematics. Dr. Ben Nasser joined the research group

Amjad Felemban

Areas of Expertise and Research Interests ​Control Systems Robotics Multi-Agent Systems Machine Learning Education Profile ​BS in Electrical Engineering, Purdue University, USA​ (2016)

Amin Almozel

Education 2021.01 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA 2018.08 - 2019.12 M.S. in Electrical and Computer Engineering, KAUST, KSA 2014.08 - 2018.05 B.S. in Electrical and Computer Engineering, University of Rochester, USA Selected Publications A. Almozel, J.S. Shamma, "Competitive Drone Racing Using Asymmetric Games" IEEE 18th International Conference on Ubiquitous Robots (UR), 349-356, 8 (2021). DOI: 10.1109/UR52253.2021.9494629 Areas of Expertise and Research Interests Autonomous and Intelligent Systems​

Alfiya Kulmukhanova

Areas of Expertise and Research Interests Multi-agent systems Smart Grids Game theory applications for Smart Grids Education Profile Bachelors in Electrical and Electronic Engineering, Nazarbayev University, Kazakhstan (2016)
<> visual computing (33)

KAUST Cell Visualization Summit 2019

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You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Optimal Reference Frames, Scaling and Features in Visualization

Prof. Holger Theisel, Visual Computing, Magdeburg University

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B9 L2 H1 R2322

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Enabling Accurate and Efficient Simulation in Visual Computing - Public Colloquium

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KAUST

The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations. This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Masterclass on Visual Computing

Dominik Michels, Helmut Pottmann, Ivan Viola, Peter Wonka, Soeren Pirk, Wolfgang Heidrich

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KAUST

Visual Computing has become a key enabling technology for a diverse set of applications spanning scientific discovery, digital services, medicine, robotics, consumer electronics, and entertainment, to name just a few. The research community tackles problems in this vast space by drawing from expertise in multiple disciplines, including Computer Science, Electrical Engineering, and Mathematics. The KAUST Masterclass on Visual Computing highlights a selection of cutting-edge academic research within this field by comprising a series of talks focusing on different topics ranging from Computational Architecture and Fabrication, Deep Optics, and Generative Modeling, to Nanovisualization, Physics-based simulation, and Representation Learning.

Workshop on Applied Geometry and Visual Computing

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The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.

Physics-based Simulation in Visual Computing

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This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Peter Wonka's new query tool, called a relation-augmented image descriptor will change forever querying images online.

1 min read ·
Today, querying the massive amounts of images available in online databases such as Instagram can be a time-consuming experience. Researchers from the King Abdullah University of Science and Technology (KAUST) and the University College London, have developed a new tool that generates image queries based on a geometric description of objects in spatial relationships with potential applications in computer graphics, computer vision and automated object classification.

Visualization helps science to see the unexpected

1 min read ·
Advances in how science is presented means that visual tools can inspire research, as well as make its results accessible to the world.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Polymers offer a better view

1 min read ·
A window opens for analyzing the distribution of small molecules in biological and medical tissue samples.

Teaching algorithms to see

1 min read ·
Four hundred hours of video are uploaded to YouTube every minute. Other than using text to search the title, description or tags associated with a video, video content search is limited. In addition to helping users find content more quickly and accurately, the ability to search video content is of paramount importance to video platforms and advertisers. Last year, YouTube was embroiled in controversy after rolling video ads for Coca-Cola and Amazon, among others, were shown before racist and extremist content. Advertising content is a major revenue stream for video platforms like YouTube

Nanovisualization Research Group is hiring

1 min read ·
Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Scanning in the Fourth Dimension

1 min read ·
Three-dimensional (3D) computed tomography is a widely used technology that visualizes an object's external and internal structure by assembling a series of two-dimensional images taken sequentially across or around it. However, as anyone who has had a medical magnetic resonance imaging scan will recall, this type of 3D reconstruction requires the subject to be motionless throughout the capture process, which can take minutes. Capturing a 3D structure that changes or deforms over time is much more difficult, and existing approaches often yield reconstructions marred by image artifacts and

Professor Ivan Viola Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Professor Ivan Viola has joined VCC as an Associate Professor of Computer Science in November, 2018. He is establishing the Nano-visualization Research Group as a part of the Visual Computing Center. Prof. Viola has a master and Ph.D. from TU Wien in Austria. He has received a series of honors and recognition for his contribution to computing visualization such as the Austrian Computer Graphics Award 2016 for the Best Technical Solution, and the 1st Place Eurographics Dirk Bartz Prize for Visual Computing in Medicine 2013. Prof. Viola was attracted to KAUST for

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

Making the invisible, visible

1 min read ·
As bioscience and medicine progress, we are now able to live with diseases, which were once a death sentence. And we are rapidly discovering new interactions at the microscopic level that give us insight into the engines of life. However, as research gets more complicated, the layperson gets left further and further behind. Even those who trust the work of scientists can feel overwhelmed when attempting to read the latest research.

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

First 3D Visualizations of SARS-CoV-2 Cryo-EM Tomography Data

1 min read ·
Together with our partners from Tsinghua University and Nanographics, our group has succeeded in depicting the first 3D visualizations of Cryo-EM microscopy tomography directly from the data. We are advancing the technology to allow for clear, noise-free visualization of this challenging data modality. Once we succeed with that our next challenge is to integrate such 3D visualization into the structural modeling process. Stay tuned!

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Yuchen Li

Ph.D. Student, Computer Science
Yuchen Li is a PhD Student under the supervision of Professor Mohamed Elhoseiny in Vision-CAIR Group at Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before joining KAUST, Yuchen was a research intern at iFLYTEK, an intelligent speech and artificial intelligence company in Hefei, China, for three months. He was a Rocket MQ open source contributor and Alibaba summer of code student developer with rich research and engineering experience. He visited NUS, UBC, THU, and other prestigious universities for academic exchanges with a

Markus Hadwiger

Professor, Computer Science
Markus Hadwiger is a Professor of Computer Science at the King Abdullah University of Science and Technology (KAUST). A founding member of the University, he conducts fundamental and applied research in scientific visualization and visual computing.

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.

Hisham Alhussain

Hisham Alhussain is a Computer Science Master Student under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hisham received his bachelor's degree in Computer Science from the University of Massachusetts at Amherst in 2018. He is also a Software Developer at IBM. Research Interest He's currently focusing on Scientific Visualization and Visual Computing. He is proficient in Python, Java, and has professional experience in React/React-Native and mobile and web development

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Dmitry Lyakhov

Dmitry Lyakhov is a Research Scientist at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), working with Professor Dominik Michels in the Computational Sciences Group (CSG). Education and Early Career Dmitry Lyakhov graduated from Belarusian State University with a master's degree in Theoretical Physics and, later on, a Ph.D. in Mathematics. He is also an alumnus of the National Academy of Sciences of Belarus and visiting scholar of von Karman Institute for Fluid Dynamics. In 2018, he received a Nanodegree in Machine Learning. Before joining KAUST

Cristhopper Armenta

Originally from Mexico, Cristhopper is a computing professional who has a keen interest in mobile platforms like iOS, Android and Windows Phone. His specialties and interests range from visual computing (visualization), computer graphics, mobile technology development and technology business development. After some contractor work with two companies in Mexico and the US, he now works for IBM in the US. ''I worked as a subcontractor for IBM and got to meet some of the key people for the project who really liked my work and ended up offering me a full-time position,'' he told us. How was life at
<> visualization (33)

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Interactive High-Quality Visualization of Large-Scale Particle Data

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B2 L5 R5209

Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

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B1 L2

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud

KAUST Cell Visualization Summit 2019

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B9 H2 R2325

You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Optimal Reference Frames, Scaling and Features in Visualization

Prof. Holger Theisel, Visual Computing, Magdeburg University

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B9 L2 H1 R2322

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Visualization Methods for Graphs&Networks

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B9 L2 H2 H2

Inevitably, the projection of most graph structures on two-dimensional screens will create errors and therefore visually wrong impressions. In the past, two types of methods have been developed to minimize projection errors and distribute them in a visually pleasing way. The first group of methods, force-directed layouts, interpret the links of a graph as physical springs, while stress-based methods minimize an energy function, which aims to map graph distances faithfully.

A breeding ground for successful start-ups

1 min read ·
State-of-the-art facilities and world-class research projects give rise to thriving start-up companies based at KAUST.

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read ·
Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Visualizing a real change: CEMSE-held exhibition "State of Flux" opens new frontiers in information visualization for science

1 min read ·
Climate change is affecting the world drastically. Contrasting perceptions look at it as either a direct effect of human carelessness or a natural calamity. Whatever the case, prompting action for preventing or mitigating its effects lies on effective and easy-to-understand communication.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Nanovisualization Research Group is hiring

1 min read ·
Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Scanning in the Fourth Dimension

1 min read ·
Three-dimensional (3D) computed tomography is a widely used technology that visualizes an object's external and internal structure by assembling a series of two-dimensional images taken sequentially across or around it. However, as anyone who has had a medical magnetic resonance imaging scan will recall, this type of 3D reconstruction requires the subject to be motionless throughout the capture process, which can take minutes. Capturing a 3D structure that changes or deforms over time is much more difficult, and existing approaches often yield reconstructions marred by image artifacts and

Professor Ivan Viola Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Professor Ivan Viola has joined VCC as an Associate Professor of Computer Science in November, 2018. He is establishing the Nano-visualization Research Group as a part of the Visual Computing Center. Prof. Viola has a master and Ph.D. from TU Wien in Austria. He has received a series of honors and recognition for his contribution to computing visualization such as the Austrian Computer Graphics Award 2016 for the Best Technical Solution, and the 1st Place Eurographics Dirk Bartz Prize for Visual Computing in Medicine 2013. Prof. Viola was attracted to KAUST for

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

The NANOVIS team wins the 1st Place in Graph Drawing Contest 2019

1 min read ·
The NANOVIS team has won the 1st Place in Graph Drawing Contest 2019 under the category of "Creative Topic #2 Meal Ingredients" on their graph "Worldmap of Food". The contest took place in the 27th International Symposium on Graph Drawing and Network Visualization (GD'19) that was held from September 17 to 20, 2019 in Průhonice/Prague, Czechia. Check the full poster:

Three papers with contributions from the NANOVIS team will be presented at IEEE VIS

1 min read ·
Three papers with contributions from the NANOVIS team will be presented at IEEE VIS 2019 that will be held from 20 to 25 October 2019 in Vancouver, Canada. Multi-Scale Procedural Animations of Microtubule Dynamics Based on Measured Data ScaleTrotter: Illustrative Visual Travels across Negative Scales Data-aware Continuous Colormap Refinement for 2D Scalar Field Visualization IEEE VIS 2019 is the premier forum for advances in visualization for academia, government, and industry. This week-long event brings together researchers and practitioners with a shared interest in visualization.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Thomas Hollt

Postdoctoral Research Fellow, High Performance Visualization Group
Thomas Hollt was a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor at Computational Biology Center in Leiden University Medical Center, The Netherlands. Education and Early Career Thomas joined KAUST as a Ph.D. student in Computer Science under the supervision of Professor Markus Hadwiger. After he graduated in 2013, he worked as a Postdoctoral Research Fellow in Visual Computing Center. Later on, he joined the Earth Fluid Modeling and Prediction Group at KAUST as a Research

Ronell Sicat

Visualization Scientist, Core labs and Research Infrastructure
Ronell Sicat is a scientist with more than 10 years of experience in scientific research and software development. His main interests are scalable visualization and analysis of large-scale data (e.g., images, volumes, meshes), and immersive analytics that leverage augmented/virtual reality. He is passionate about helping domain scientists solve their problems using my wide-ranging experience in programming, image processing, segmentation, quantitative analysis, and computer graphics.

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Matej Mlejnek

Research Scientist, Computer Science
Matej Mlejnek is a Research Scientist in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his bachelor degree in Automation and Control from Slovak University of Technology in Slovakia in1999. After that, he joined Vienna University of Technology in Austria and received both his master (2003) and doctoral (2006) degrees in Computer Science. Research Interest Matej is interested in scientific visualization. Selected Publications Matej Mlejnek has many

Markus Hadwiger

Professor, Computer Science
Markus Hadwiger is a Professor of Computer Science at the King Abdullah University of Science and Technology (KAUST). A founding member of the University, he conducts fundamental and applied research in scientific visualization and visual computing.

Marco Agus

Marco Agus was a Research Engineer in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his master degree in Electronic Engineering from the University of Cagliari in Italy in 1999. He has a Ph. D. in Mechanical Design from the University of Cagliari. Before joining KAUST, Marco was a researcher at Visualization and Virtual Reality Group in the Center for Advanced Studies, Research and Development in Sardinia (CRS4) in Italy. After that in 2007, he moved to

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.

Jens Schneider

Research Scientist, Computer Science
Jens Schneider was a Staff Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Qatar. Education and Early Career Jens Schneider received a diploma equivalent to Master degree in Computer Science from RWTH Aachen in Germany, 2004. He earned his Ph.D. in Computer Science from Technical University Munich, Germany, in 2009 Dr. Schneider joined KAUST in 2009 as a Postdoctoral Research Fellow. Later on, he was promoted to Research

Donggang Jia

Ph.D. Student, Computer Science
Donggang Jia is a Ph.D. student in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Donggang has a Master's Degree in Artificial Intelligence from the University of Southampton, United Kingdom. Before that, he received his Bachelor's degree in Computer Science at the Beijing University of Posts and Telecommunications, China. Research Interest Donggang is focusing on computer graphics and visualization

Dawar Khan

Postdoctoral Research Fellow, Computer Science
Dawar Khan has been working as a Postdoctoral researcher with Professor Ivan Viola in the Nanovisualization Research Group, at Visual Computing Center, KAUST - King Abdullah University of Science and Technology, Saudi Arabia, since April 2022. Previously he worked as assistant professor at Interactive Media Design Lab, Nara Institute of Science and Technology (NAIST), Japan and at the University of Haripur, Pakistan. Education and Early Career He received the BS and MS degrees in computer science from the University of Malakand, Pakistan, in 2011 and 2014, respectively, and the PhD degree in

Cristhopper Armenta

Originally from Mexico, Cristhopper is a computing professional who has a keen interest in mobile platforms like iOS, Android and Windows Phone. His specialties and interests range from visual computing (visualization), computer graphics, mobile technology development and technology business development. After some contractor work with two companies in Mexico and the US, he now works for IBM in the US. ''I worked as a subcontractor for IBM and got to meet some of the key people for the project who really liked my work and ended up offering me a full-time position,'' he told us. How was life at

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Amal Aboulhassan

Amal Aboulhassan was a Ph.D. student in Computer Science under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is the founder of MaterialSolved startup located in Alexandria, Egypt. Education and Career Amad received her Master degree in Computer Graphics from Alexandria University, Egypt in 2005. She joined KAUST and earned her Ph.D. in Computer Science in 2017. She was a part of High Performance Visualization Research Group under the supervision of Professor Markus Hadwiger. Before joining

Alexandre A. Kouyoumdjian

Research Scientist, Computer Science
Dr. Alexandre A. Kouyoumdjian has joined the Nanovisualization Research Group as a Research Scientist working with Professor Ivan Viola. His main research interests are Visualization & Computer Graphics, Human-Computer Interaction & Virtual Reality, and Microarchitecture. Dr. Alexandre A. Kouyoumdjian has also worked in the industry as an R&D Consultant for a start-up that develops VR applications meant to ease chronic pain and a graphics programmer for a game studio. Education Profile Ph.D., Computer Science, Université Paris-Saclay,FRANCE, 2018 M.S., Computer Science (Bioinformatics and
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Autonomy for recreational vessels: Safe intelligence at your finger tips Pablo Afman, ATL Robotics

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B2 L5 R5220

Abstract Thanks to ever improving sensing, computing, and actuating technologies, autonomy is fast becoming a practical tool for many marine applications. Pushed by the general interest in autonomy and attracted by its underlying capabilities, this talk will present robotic technologies that enable safety and convenience for recreational boaters and marine professionals alike. Beginning with a Grady-White offshore fishing boat powered by two 425 HP engines, we present demonstrated functionalities such as collision avoidance, auto-docking, dynamic positioning, track and follow, and "take me

CEMSE Welcomes new Professor: Jeff Shamma

1 min read ·
The CEMSE Division has recently welcomed a new Professor of Electrical Engineering, Dr. Jeff Shamma. Prof. Shamma joins from the Georgia Institute of Technology (Georgia Tech).

Jeff Shamma elected IFAC fellow 2014-17

1 min read ·
The award is an international distinction given to scientists for their contributions in the fields of interest of IFAC under the form of technical publications, patents, control solutions, products, software, and leadership in research, development, and education.

Team KAUST places 3rd at the Mohamed Bin Zayed International Robotics Challenge

1 min read ·
A team from the King Abdullah University of Science and Technology (KAUST) competed in the inaugural Mohamed Bin Zayed International Robotics Challenge (MBZIRC), held in Abu Dhabi, UAE, March 2017, and took a 3rd place finish in the ground robotics challenge.

KAUST hosts International Graduate School on Control event

1 min read ·
KAUST recently acted as a host campus for the European Embedded Control Institute's International Graduate School on Control (IGSC). The IGSC is an annual series of 27 one-week graduate modules focusing on different topics of networked and embedded control and is taught to eligible attendees at different locations worldwide. The series is co-sponsored by the Institute of Electrical and Electronics Engineers Control Systems Society and the International Federation of Automatic Control.

Professor Shamma awarded International Federation of Automatic Control High Impact Paper Award

1 min read ·
KAUST Professor of Electrical Engineering Jeff Shamma has been awarded The International Federation of Automatic Control Council’s (IFAC) High Impact Paper Award 2020.

Professor Park receives O. Hugo Schuck Best Paper Award at the 2022 American Control Conference

1 min read ·
Professor Shinkyu Park has been awarded the 2022 O. Hugo Schuck Best Paper Award (Theory) from the American Automatic Control Council (AACC) for his paper investigating a new mathematical framework to study decision making in multi-agent systems.

Usman Amin Fiaz

Areas of Expertise and Research Interests Robotics Intelligent Systems Design Multi-Agent Control Systems Aerial Grasping Self-Assembly & Self-Reconfigurable Systems ​ Education Profile PhD in Electrical Engineering, University of Maryland, College Park (UMD), United States of America (current) MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) BS in Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan (2015)

Shinkyu Park

Professor Park advances robotic science by building on individual robots’ core sensing, actuation and communication capabilities in distributed information processing, decision-making and manipulation.

Sarah A. Toonsi

Areas of Expertise and Research Interests Robotics Control Systems Programming Education Profile ​BS in Electrical and Computer Engineering, Effat University, Saudi Arabia (2017) ​

Samet Guler

Postdoctoral Research Fellow, Electrical and Computer Engineering
Samet Güler (M’16) received the B.S. degree in electrical and electronics engineering from Eskisehir Osmangazi University, Turkey, in 2010, the M.S. degree in electrical and electronics engineering from Bilkent University, Turkey, in 2012, and the Ph.D. degree in mechanical and mechatronics engineering from University of Waterloo, Waterloo, ON, Canada, in 2015. He worked as a short term Researcher at the University of Waterloo in 2016. He is currently a Postdoctoral Fellow at the Robotics, Intelligent Systems and Control Lab in King Abdullah University of Science and Technology, Kingdom of

Noureddine Toumi

Areas of Expertise and Research Interests Robotics Control Multi-agent systems Cooperative aerial transportation Education Profile BS ​Engineering from Tunisia Polytechnic School, Major: signal processing,systems conception and control.

Nawaf Alotaibi

Education 2020.08 - 2022.05 M.S. Student in Mechanical Engineering, KAUST, KSA 2016.08 - 2020.05 B.S. in Mechanical Engineering, Georgia Institute of Technology, USA Professional Profile 2019.06 - 2019.08 Research Intern, RISC Lab, KAUST, KSA Areas of Expertise and Research Interests ​Robotics Autonomous Vehicles Machine learning Vision

Mohammed M. Alrashed

Mohammed M. Alrashed research interests are in ​Systems and Control with robotic applications and Mathematical Theory of Rational Behavior. Master Thesis Abstract The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. The lack of understanding of panic stampedes still causes hundreds of fatalities each year, not to mention the scarce methodical studies of panic behavior capable of envisaging such crowd dynamics. Under those circumstances, there are thousands of

Mohammed Abdullah Algarni

Areas of Expertise and Research Interests Unmanned Aerial Vehicle (UAV) Robotics Autonomy Aerial Landing Education Profile MS in Mechanical Engineering, King Abdullah University for Science and Technology (KAUST), Saudi Arabia (2014​​) ​BS in Mechanical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia (2009)

Mohammad Aljohany

Education 2021 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA M.S. in Electrical and Computer Engineering , KAUST, KSA B.S. in Instrumentation and Control Engineering, Yanbu Industrial College, KSA Areas of Expertise and Research Interests Control Theory Intelligent Systems Automation Reinforcement Learning

Mohamed A. Mabrok

Postdoctoral Research Fellow, Electrical and Computer Engineering
From 2015 till 2017 Mohamed A. Mabrok was a research associate at Robotics, Intelligent Systems & Control (RISC) research group at King Abdullah University of Science and Technology (KAUST). Currently, he is an Assistant Professor at the Faculty of Engineering at the Australian College of Kuwait. He obtained his Ph.D. in the field of systems and control in 2013 from the University of New South Wales at Australian Defence Force Academy, Canberra, Australia. Mohamed also holds a bachelor degree in applied mathematics and a master degree in applied mathematics/quantum physics. Mohamed's research

Karnika Biswas

Education 2021.09 - 2021.12 Visiting Intern (Research), KAUST, RISC,KSA 2013.1 - 2021.11 Ph.D. in Robotics and Control (EEE), IIT Guwahati, India 2010.06 - 2012.06 M.Tech in Mechatronics, IIEST, Shibpur (BESUS), India 2003.08 - 2007.08 B.Tech. In Electronics and Communication Engineering, MAKAUT (WBUT), India Professional Profile 2018.05 - 2018.08 Research Scientist, WHODAT, India 2014.01 - 2018.01 Teaching Assistant, EEE, IIT GUWAHATI, India 2008.01 - 2009.12 Lecturer, ECE, Institute of Engineering and Management, India Areas of Expertise and Research Interests Robotics and Control Navigation

Khalid Alzuhair

M.S. Degree, Electrical and Computer Engineering, KAUST (King Abdullah University of Science and Technology).

Jiming Jiang

Areas of Expertise and Research Interests Circuit and System, Robotics, Wireless Sensor Network Education Profile PhD in Electronics, Cambridge University, United Kingdom (2010) MS in Analog and Digital Integrated Circuit Design, Imperial College, United Kingdom (2004​) BS in Electronic Engineering, Beijing University of Technology, China (2003)​

Haya Alsharif

Areas of Expertise and Research Interests ​​Control Systems Computer Vision Machine Learning Robotics Education Profile ​​BS in Electrical and Computer Engineering, King Abdulaziz University (KAU), Saudi Arabia (2016)

Hassan Jaleel

Postdoctoral Research Fellow, Electrical and Computer Engineering
Areas of Expertise and Research Interests ​Distributed control of multi agent systems using game theory and algebraic graph theory Energy aware algorithms for multi agent systems Cyber physical systems Stochastic geometry Coverage problems for sensor networks Education Profile PhD in Electrical & Computer Engineering, Georgia Tech (2013) MS in Electrical & Computer Engineering, Georgia Tech (2011) BSc in Electrical Engineering, UET (2007)

Hassan S. Abdelraouf

Ph.D. Student, Mechanical Engineering
Education 2018.09 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2015.01- 2017.01 M.S. in Aerospace Engineering, Cairo University, Egypt 2010.09 - 2014.05 B.S. in Aerospace Engineering, Cairo University, Egypt Professional Profile 2021.01 - 2021.05 Teaching Assistant, ECE, KAUST, KSA 2020.08 - 2020.12 Teaching Assistant, ECE 376, ECE, KAUST, KSA 2020.01 - 2020.05 Teaching Assistant, ECE 372, ECE, KAUST, KSA Honors and Awards 2019.12 "Best Aerial Team" award in the European Robotics League team competition Areas of Expertise and Research Interests Control Theory Aerospace systems

Gilberto Mendoza Chavez

Areas of Expertise and Research Interests Robotics Control Systems Distributed Systems Education Profile Masters in Electrical Engineering, King Abdullah University of Science & Technology, KSA (2016)​ ​Bachelors in Mechatronics Engineering, Monterrey Institute of Technology and Higher Education, Mexico (2014)

Doha Raik Hamza Mohamed

Introduction ​Doha Raik Hamza Mohamed remained at KAUST as Associate Research Scientist under the supervision of Prof. Je Shamma after graduation of Ph.D. from the CTL group at KAUST in 2014. Education and early career Doha Raik obtained her B.S. in Communications and Electronics Engineering, Alexandria University, Alexandria, Egypt in 2001. Then in 2008, she went to Nile University, Cairo, Egypt to get M.S. in Wireless Communications. After graduation in 2010, she earned a Ph.D. degree in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal. Research

Brian Parrott

Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, King Abdullah University of Science and Technology, KSA 2017.06 - 2019.06 MBA, HKUST, Hong Kong-China ​2010.08 - 2011.12 MS in Chemical and Biological Engineering, KAUST, KSA 2006.09 - 2010.06 BS in Biology, University of Washington, USA Professional Profile 2012.05 - Present Research Scientist, Research and Development Center, Saudi Aramco, KSA Honors and Awards 2014.01 CEO Excellence Award 2013.01 International Glory Medal, International Federation of Inventors' Associations Selected Publications B. Parrott, P. Carrasco

Angelica Coronado Preciado

Anwaar Alghamdi

‌ Education: B.Sc., Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia, 2017 Research Interests: Control systems Autonomous robotics Wireless sensor networks

Amjad Felemban

Areas of Expertise and Research Interests ​Control Systems Robotics Multi-Agent Systems Machine Learning Education Profile ​BS in Electrical Engineering, Purdue University, USA​ (2016)

Amin Almozel

Education 2021.01 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA 2018.08 - 2019.12 M.S. in Electrical and Computer Engineering, KAUST, KSA 2014.08 - 2018.05 B.S. in Electrical and Computer Engineering, University of Rochester, USA Selected Publications A. Almozel, J.S. Shamma, "Competitive Drone Racing Using Asymmetric Games" IEEE 18th International Conference on Ubiquitous Robots (UR), 349-356, 8 (2021). DOI: 10.1109/UR52253.2021.9494629 Areas of Expertise and Research Interests Autonomous and Intelligent Systems​

Alfiya Kulmukhanova

Areas of Expertise and Research Interests Multi-agent systems Smart Grids Game theory applications for Smart Grids Education Profile Bachelors in Electrical and Electronic Engineering, Nazarbayev University, Kazakhstan (2016)
<> modeling (31)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

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B9 L2 H1 R2322

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

Modeling and Numerics of Sorption Kinetics

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B1 L3 R3119

An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Professor Meriem Laleg's research on modeling contaminant transport published in SIAM Journal on Scientific Computing

1 min read ·
A new algorithm is proposed to estimate the average velocity, dispersion coefficient, and differentiation order of a space-fractional advection-dispersion equation used for modeling contaminant transport in porous media. This allows for the characterization of the medium and the determination of the contaminant source. The algorithm is efficient, robust and fast.

Professor Meriem Laleg's research on brain response models published in Journal of Neuroscience Methods

1 min read ·
A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements.

Professor Meriem Laleg's research on membrane distillation modeling for desalination published in Desalination

1 min read ·
An accurate mathematical model is proposed to describe an emerging desalination technology called direct contact membrane distillation system. The mathematical model is important for designing efficient control and monitoring strategies, a crucial step to facilitate the commercialization of this technology.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

KAUST Associate Professor Taous-Meriem Laleg-Kirati finalist at Leadership Excellence for Women Awards & Symposium

1 min read ·
Taous-Meriem Laleg-Kirati, KAUST associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Science and Engineering Division, was selected as one of three finalists in the academic of distinction category at the recent Leadership Excellence for Women Awards & Symposium (LEWAS) in Bahrain.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Faculty Focus: Taous-Meriem Laleg-Kirati

1 min read ·
-By A. Ortega, KAUST News KAUST Associate Professor of Electrical Engineering Taous-Meriem Laleg-Kirati joined the University in 2011 after working as a researcher at INRIA in France. Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group at KAUST, and her interests lie in control theory, system modeling and signal applications. She investigates multiple engineering disciplines and develops mathematical models and algorithms that control processes relying on real-time feedback. "My research goals [are the development of] effective estimation methods and algorithms to

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Viruses as communication molecules

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Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Competition sheds light on approximation methods for large spatial datasets

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Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Watching viscous flow, but faster

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A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Otavio Bertozzi

Research Interests Control systems and power systems engineering. Modeling, simulation, analysis and design of solutions applied to power system stability and control. Optimal and data-driven approaches to the integration of renewable energy resources. Education Profile M.Sc., Electrical and Computer Engineering, KAUST, Thuwal, Saudi Arabia, 2020 ​B.Sc., Electrical Engineering, Unesp, Bauru, Brazil, 2019 Professional Profile 2017-2018: xDSL Interoperability Testing Intern, Intel Corporation, Munich, Germany 2015: Summer Intern, Rohde & Schwarz Reference Laboratory (BME HVT), Budapest, Hungary

Matthew McCabe

Research Interests Prof. McCabe’s research focuses on issues related to water and food security, climate change impacts, precision agriculture, water resources monitoring and modeling, and the novel use of technologies for enhanced Earth system observation. The research undertaken in his group combines models and observations to answer questions on the distribution, variability and exchanges of water at local, regional and global scales, as well as the interactions with vegetation. CubeSats, unmanned aerial vehicles (UAVs) and in-situ monitoring techniques are all employed to monitor

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.

Evangelos Piliouras

Education and early career Evangelos received his integrated master's degree with excellency, from the department of Electrical Engineering and Computer Technology in Patras in 2018. He recently joined King Abdullah University of Science and Technology (KAUST) and he works under the supervision of Prof. Meriem Laleg in the field of estimation. Evangelos did a 3-month internship in Dialog Semiconductor as a software developer, having developed a software automation tool. Research Interests Evangelos' research interests are found in the area of biosensors, embedded system design, modeling and

Asem Alalwan

Asem Alalwan is a Ph.D. candidate in the Estimation, Modeling, and Analysis group (EMAN) supervised by Prof. Meriem Laleg at King Abdullah University of Science and Technology (KAUST). Education and Early Career Asem has graduated with a bachelor degree in Electrical Engineering from King Fahad University of Petroleum and Minerals KFUPM) in 2014. He joined KAUST in 2014 and earned his Master degree in 2016. Research Interests Asem's research is focused in the area of control and design of Free space optical (FSO) communication systems. FSO has emerged as a viable technology that offers a large
<> numerical analysis (31)
KAUST-CEMSE-NumPDE-Workshop-2024

NumPDE Workshop: Numerical Analysis of PDEs

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Sunday 26/01 (morning, 8.45-13.30) - Auditorium between Building 2 and Building 3; Sunday 26/01 (afternoon, from 13.45) - Building 9, Room 2322 (Lecture Hall); Monday 27/01 and Tuesday 28/01 - Auditorium between Building 4 and Building 5

Explore cutting-edge numerical analysis of PDEs, including Finite Element Methods, Virtual Element Methods, and Isogeometric Analysis, with leading experts.
Georgios Grekas

Georgios Grekas

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

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B1 L4 R4214

Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to

An introduction to Patankar-type schemes

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B9 L3 R3229

We present order conditions for various Patankar-type schemes as well as a new stability approach that examines the non-hyperbolic fixed points of the schemes for a general linear test problem. We formulate sufficient conditions for the stability of such non-hyperbolic fixed points and the local convergence of the numerical approximation towards the correct steady-state solution of the underlying test problem. To illustrate the theoretical results, we consider several members of the modified Patankar-type family within numerical experiments.

Hierarchical ensemble-based data assimilation and rare event tracking

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B4 L5 R5220

This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.

Sadeed B. Sayed and Ismail E. Uysal ranked in the student paper competition at the ACES Conference

1 min read ·
Ph.D. students Sadeed B. Sayed and Ismail E. Uysal working in the Computational Electromagnetics Group led by Prof. Hakan Bagci ranked 3rd and 5th in the student paper competition at the 30th International Conference

KAUST Associate Professor Taous-Meriem Laleg-Kirati finalist at Leadership Excellence for Women Awards & Symposium

1 min read ·
Taous-Meriem Laleg-Kirati, KAUST associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Science and Engineering Division, was selected as one of three finalists in the academic of distinction category at the recent Leadership Excellence for Women Awards & Symposium (LEWAS) in Bahrain.

CEMSE New Faculty: Daniele Boffi, Professor of Numerical Analysis

1 min read ·
Daniele Boffi is a professor of numerical analysis who recently joined the KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division.

Understanding the COVID wave

1 min read ·
As countries around the world work to "flatten the curve," a reference to slowing the progression of COVID-19 cases, David Ketcheson is putting mathematical modeling to work to understand how transmission is playing out.

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

KAUST mathematician named Fellow to European Academy of Sciences

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, is named a 2020 Fellow to the European Academy of Sciences (EurASc) in recognition of his outstanding scholarship in the field of mathematical and numerical analysis of partial differential equations.

Mathematician Peter Markowich named 2022 AMS Fellow

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich joins 45 mathematical scientists from around the world named 2022 Fellows of the American Mathematical Society (AMS) program.

Yang Liu

Education Profile B.Eng. Aerospace Engineering, Beihang University, Beijing, China, 2017. M.Sc. Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2019. Research Interests Yang's research interests include ​Probability Theory and Stochastic Processes, Numerical Analysis.

Waleed Alhaddad

Waleed Alhaddad obtained his Master's degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Raul F. Tempone at Stochastic Numerics Research Group (STOCHNUM). Research Interests Waleed's research interests include ​Mathematical Modeling, Probability Theory and Stochastic Processes, Numerical Analysis. Education Profile ​M.Sc. Applied Mathematics, Finance, King Abdullah University of Science and Technology (KAUST) 2016-2018. B.Sc. Physics, University of California Santa Barbara 2016. Awards and

Vaibhav Mehandiratta

Postdoctoral Research Fellow, Statistics
Vaibhav Mehandiratta is a Post-Doctoral fellow In the CEMSE Division, Statistics program, at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor David Bolin. Vaibhav received his PhD from the Department of Mathematics, Indian Institute of Technology Delhi in November 2022. He joined KAUST as a Post-Doctoral fellow in January 2023.

Sören Wolfers

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Shuyu Sun

Shuyu Sun is a professor of Earth Science and Engineering and an affiliate professor of Applied Mathematics and Computational Science at KAUST. He specializes in computational methods for modeling subsurface flow and transport phenomena. His research focuses on advancing numerical techniques for large-scale geophysical simulations, with key applications in energy resources and environmental science.

Sadeed Bin Sayed

Sadeed Bin Sayed is a Ph.D. candidate in Electrical engineering at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Hakan Bagci. He received his B.Tech. degree in electronics and communication engineering from the National Institute of Technology Calicut, Calicut, India, in 2004, and the M.Tech. degree in communications engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 2007. From July 2007 to May 2008, he worked as a Member with Technical Staff Digibee Microsystems, Bangalore, India. In May 2008, he joined as an

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Najwa Alshehri

Career: Lecturer at the Royal Commission for Jubail and Yanbu. (current) PhD in Applied Mathematics and Computational Sciences. (current) Other interests: Beyond my academic and research pursuits, I am deeply committed to mentoring and empowering female students to pursue careers in science and research. At KAUST, I actively engage in initiatives that cultivate talent in mathematics, AI, and deep learning. Additionally, I place significant emphasis on developing my leadership and entrepreneurial skills through specialized programs and certifications along with other trainings in Key

Linda Alzaben

Linda Alzaben is a Ph.D. candidate in Applied Mathematics and Computational Science. Linda has a Bachelor's and a Master's degree, both in pure Mathematics, from King Abdulaziz University. In both her B.Sc. and M.Sc. she was first-class honors. Although she had been teaching for several years she decided to pursue her postgraduate studies in an engaging and interesting field at KAUST. She seized the opportunity to study and work under the supervision of Professor Daniele Boffi who is a distinguished professor in his field. Her main interest are in Finite elements and Mixed Finite elements and

Juncai He

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application

Jongho Park

I am a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematical Sciences/Computer Science in 2013, and M.S. and Ph.D. degrees in Mathematical Sciences in 2015 and 2019, respectively (Advisor: Chang-Ock Lee), all from KAIST. From 2019 to 2020, I was a postdoctoral researcher in Department of Mathematical Sciences at KAIST (Mentor: Chang-Ock Lee). From 2020 to 2023, I was a research assistant professor in Natural Science Research Institute at KAIST

Håkon A. Hoel

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Gaukhar Shaimerdenova

Gaukhar Shaimerdenova is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Gaukhar's research interests include ​Stochastic Differential Equations, Numerical Analysis, Data Assimilation, Multilevel/Multi-index Monte Carlo methods, Ensemble Kalman Filtering and Computational Finance. Selected Publications Hoel, H., Shaimerdenova, G. and Tempone, R., 202 2 . Multi -index ensemble Kalman filtering. Journal of Computational Physics. Hoel, H

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

David Keyes

Senior Associate to the President, King Abdullah University of Science and Technology
David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.
<> Stochastic Geometry (30)

Spectrum Management for Future Generations of Cellular Networks

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B1 L2 R2202

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

Ultra-Dense LEO Satellite-based Communication Systems: A Novel Modelling Technique

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B1 L3 R3119

In this talk, we discuss a recently proposed mathematical framework that enables tractable analysis of LEO satellite-enabled communication systems while capturing the influence of satellites’ numbers and altitudes as well as the spatial distribution of earth stations. Firstly, we describe how the stochastic geometry-based framework is modeled and discuss its accuracy. Next, we provide a detailed example of where this framework can be used for coverage analysis. Furthermore, we discuss how this framework can be used to study routing and end-to-end latency analysis in such networks. Realistic values from existing constellations, such as OneWeb and Starlink, are further used as case studies in this talk.

Wireless Multihop Networks: Finding the Best Way Home

1 min read ·
In contrast to conventional cellular networks, devices in wireless multihop networks have dual roles: while each device is a source of information it also acts as a router for its peers.

Dr. Hesham ElSawy recognized as Exemplary Reviewer by IEEE Transactions on Communications

1 min read ·
Dr. Hesham ElSawy was recognized by the IEEE Transactions on Communications (TCOM) as an Exemplary Reviewer. Less than 2% of all IEEE TCOM Reviewers in 2015 got this recognition.

Dr. Hesham ElSawy Elevated to Senior Member of IEEE and Recognized as an Exemplary Reviewer by the IEEE Transactions on Communications

1 min read ·
Dr. Hesham ElSawy has been elevated to the rank of Senior Member of IEEE, the world's largest technical professional organization for the advancement of technology. The achievement is considered a significant honor since it is granted only to researchers who have shown a significance and continuous performance over a period of five years in professional practice. At present, only an 8% of 400,000 IEEE members have received this honor because of their superior professional achievements.

Hesham ElSawy wins IEEE Communication Society

1 min read ·
The Internet of Everything in quickly growing and vastly enlarging its spectrum of applications. At the same time, traffic demand per-user is increasing dramatically. It is estimated that by 2020 there will be more than 50 billion devices connected. The cellular support infrastructure should be developed accordingly, making of network performance analysis a critical task. The existing elementary probability methods are currently inefficient when it comes to detecting mobility faults and interference.

Hesham ElSawy gains ISWCS Best Scientific Contribution Award 2017

1 min read ·
The imminent era of the smart world is foreseen to involve massively many power and computationally constraint wireless devices with sporadic traffic patterns. The surging Internet of things (IoT) and cyber physical systems (CPS) are clear examples that rely on massive wireless networks. Hence, realizing such extensive wireless connectivity is mandatory to unlock the potentials and reap the benefits of the foreseen smart era.

Charging ahead for electric vehicles

1 min read ·
Roads installed with wireless charging technology could become an integral feature of our cities in an electric vehicle future.

Yujie Qin

Yujie Qin is a Master student under the supervision of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Qin received her B.S. degree in University of Electronic Science and Technology of China (UESTC) in 2020. Selected Publications Y. Qin, M. A. Kishk and M. Alouini, "Performance Evaluation of UAV-enabled Cellular Networks with Battery-limited Drones," in IEEE Communications Letters, doi: 10.1109/LCOMM.2020.3013286. Professional Memberships B.S. in Electrical Engineering, University of

Waqas Aman

Visiting Student (former), Electrical and Computer Engineering
Remote Internship at King Abdullah University of Science and Technology (KAUST).

Ruibo Wang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Ruibo Wang is a Ph.D. student in Electrical and Computer Engineering and a member of the Communication Theory Laboratory (CTL) under the direction of Professor Mohamed Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ruibo Wang graduated from the University of Electronic Science and Technology of China (UESTC) with a Bachelor's degree in Engineering in 2020. Research Interest Ruibo's research interests include Stochastic Geometry, Wireless communication, and Networking. Education Profile B. S. in University of Electronic Science and

Ruslan Zhagypar

MS/PhD Student, Electrical and Computer Engineering , King Abdullah University of Science and Technology

Rabe Arshad

Visiting student at King Abdullah University of Science and Technology. Currently, he is a Ph.D. candidate at the University of British Columbia, Canada.

Osama Bushnaq

Ph.D. degree in electrical engineering at the King Abdullah University of Science and Technology (KAUST)

Nour Kouzayha

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
Research Scientist at King Abdullah University of Science and Technology.

Mustafa Kishk

Postdoctoral Research Fellow, Electrical and Computer Engineering
Mustafa Kishk is a Postdoctoral Research Fellow under the supervision of Prof. Slim Alouini at the Communication Theory Lab in King Abdullah University of Science and Technology (KAUST). Education and Early Career Mustafa Kishk received his Ph.D. in Electrical Engineering from the Bradley department of Electrical and Computer Engineering at Virginia Tech under the supervision of Prof. Harpreet Dhillon in 2018. He received his B.Sc. and M.S. degree in Electronics and Electrical Communications Engineering from Cairo University, Egypt, in 2013 and 2015 respectively. Research Interest Mustafa’s
Mostafa Emara

Mostafa Mohamed Lotfy Emara

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
PhD Student, Electrical and Computer Engineering at KAUST (King Abdullah University of Science and Technology)

Maurilio Matracia

Maurilio Matracia is a doctoral student under Dr. Mustafa Kishk’s and Prof. Mohamed-Slim Alouini’s supervisions at the Communication Theory Lab (CTL) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Maurilio studied at the University of Palermo (Italy), where he received both a B.S. and a M.S. degrees in Energy Engineering (2017) and Electrical Engineering (2019), respectively. His bachelor thesis involved evolutionary algorithms in order to study the influence of the soil non-homogeneity on the design of grounding systems. His master thesis based on

Laila Hesham Afify

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
Postdoctoral Research Fellow Electrical and Computer Engineering (KAUST), Assistant Professor, School of Engineering and Applied Sciences, Nile University, Egypt

Konpal Shaukat Ali

Introduction Konpal went to the University of Manitoba, Winnipeg, Canada as a Postdoctoral Research Fellow. Education and Early Career Konpal got her B.S. in Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan in 2012. After graduation, in 2012, she earned a Ph.D. degree. at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia in Electrical Engineering, and joined the University of Manitoba, Winnipeg, Canada, as a Postdoc-toral Fellow after graduation. Research Interests Device to Device Communication. Stochastic Geometry

Hui Zhao

Hui Zhao is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hui Zhao obtained his bachelor degree in Telecommunications Engineering from Southwest University (SWU) in 2016. Research Interest Hui Zhao is focusing in the area of performance analysis of wireless communication, optimization about power allocation, stochastic geometry, spatial modulation. Education Profile MS in Electrical Engineering, Kind Abdullah University of Science and

Hesham ElSawy

Postdoctoral Research Fellow, Communication Theory Lab
From April 2014 to June 2018 Hesham ElSawy was a postdoctoral fellow in the Communication Theory Lab (CTL), at the Computer, Electrical, and Mathematical Sciences and Engineering division (CEMSE), King Abdullah University for Science and Technology (KAUST), Kingdom of Saudi Arabia. He is now an assistant professor in King Fahd University of Petroleum and Minerals (KFUPM), Eastern Province, KSA Selected Publications Ph.D. Thesis H. ElSawy, “Modeling, Analysis, and Optimization of Random Wireless Networks: Stochastic Geometry Approach,” University of Manitoba, Winnipeg, MB, Canada, March 2014. M

Hao Lin

KAUST provides the best environment for us to develop. At KAUST, there are many excellent Professors and friends who can study and do research together. Communication Engineering is an interesting subject that is a key tech for the future. Communication Theory is highly worthy of our in-depth research.

Giovanni Chisci

Visiting Scholar, Communication Theory Lab
Giovanni Chisci works in the Department of Engineering, University of Ferrara, Ferrara, Italy. Selected Publications Journal Publications L. Mucchi, L. S. Ronga, G. Chisci, "Noise-Loop Multiple Access," IEEE Transsction on Vehicular Technology , vol. 65, no. 10, pp. 8255-8266, Oct. 2016. G. Chisci, H. ElSawy, A. Conti, M.-S. Alouini, M. Z. Win, "Spatiotemporal Modeling for Uncoordinated Massive Wireless Networks," Under review in IEEE/ACM Transaction on Networking. 
 G. Chisci, A. Conti, L. Mucchi, M. Z. Win, "Intrinsic Secrecy in Inhomogeneous Stochastic Networks," To appear in IEEE/ACM

Fadil Habibi Danufane

Postdoctoral Research Fellow, Electrical and Computer Engineering
Fadil Habibi Danufane is a Postdoctoral Fellow at the Communication Theory Lab (CTL) of King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Fadil Habibi Danufane received his B.Sc. degree in electrical engineering from Bandung Institute of Technology (ITB), Indonesia, in 2013, and his M.Sc. degree in computational science from Kanazawa University, Japan, in 2017. In 2021, he obtained his Ph.D. degree in telecommunications with the Laboratory of Signals and Systems in CentraleSupélec, Paris-Saclay
kaust-cemse-amcs-ctl-person-anna-talgat.avif

Anna Talgat

Anna Talgat is an MS/Ph.D. student in the Communication Theory Lab (CTL) at King Abdullah University of Science and Technology (KAUST), where she is supervised by Professor Mohamed-Slim Alouini. Her research focuses on applying stochastic geometry to satellite communication systems.

Aniq Ur Rahman

Aniq is an MS/PhD student under the supervision of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL), KAUST. Education and Early Career Aniq obtained his bachelor’s degree in Electronics and Communication Engineering from National Institute of Technology Durgapur, India in 2019. Upon graduation, he joined Qualcomm as an Associate Systems Engineer. In 2018 summers, he participated in the ALICE experiment as a research intern at the European Organization for Nuclear Research (CERN), Switzerland. Prior to joining KAUST, he worked as a Project Assistant at IISc 5G Testbed, Indian

Ainur Zhaikhan

Ainur Zhaikhan received a Master student under supervision of Professor Mohamed-Slim Alouini in the Communication Theory Lab, King Abdullah University of Science and Technology (KAUST) and will start her Ph.D. in Swiss Federal Institute of Technology Lausanne (EPFL). Education and Early Career Ainur Zhaikhan completed a bachelor degree program in Electrical and Electronic Engineering from Nazarbayev University in Kazakhstan in 2018. There she worked in Power electronics Research Lab with Nazarbayev University Associate Professor Alexander Ruderman. She was a participant of KAUST VSRP Photonics

Ahmed Bader

Research Scientist, Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST). Managing Director in Insyab Wireless Limited, Dubai, United Arab Emirates.

Abdullah Abu Zaid

Abdullah is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdullah obtained his bachelor’s degree in Electrical Engineering from The University in Jordan in 2020. During his engineering studies, he did research in wireless sensor networks and cognitive radio. Education Profile Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (2022-present) M.S. in Electrical Engineering from
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Flexible, Wearable, Disposable Wireless Communication and Sensing Systems Through Additive Manufacturing - 2019

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B9 L2 H1

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems.

Hybrid integration of materials for ultraviolet detection

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B9 L2 H1 R2322

Semiconductors are pervasive in consumer electronics and optoelectronics, and the related optical devices are deemed disruptive that Nobel Prize in Physics in 2014 was awarded to the inventors of blue light-emitting diodes (LEDs), which “has enabled bright and energy-saving white light sources”. While AlInGaN-based lasers and LEDs, and silicon-based photodetectors are currently matured, unconventional usage based on the materials has demonstrated their further potential, including solar-hydrogen generation, indoor-horticulture, and high-speed communication.

Power Electronics Applications in Offshore Wind Farms

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KAUST

Advances in power electronics have enabled many renewable energy applications. Wind energy harnessing is very promising and offshore farm installations have grown considerably in the past years. In this seminar we will go through some of the fundamentals of these enabling technologies and their applications. We will also present a simple, reliable, efficient and cost-effective concept applied to parallel connection of offshore wind turbines.

Redesigning CMOS Electronics: What, Why and How?

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KAUST

A little more than half of the world’s population enjoy benefits of information technology which is enabled by complementary metal oxide semiconductor (CMOS) electronics. Going forward, we will enjoy further augmentation of quality of life through integrated CMOS electronic systems consisting of logic, memory, communication devices, energy storage and harvester, power management units, sensors and actuators. Their main attributes will include but not limited to high performance and storage capacity for data management; seamless connectivity; energy efficiency; hyper-scale integration density; appropriate functionalities based on their applications and operational environment; reliability and safety; and finally affordability and simplicity to expand their user base to include those who do not have any access to them today. Even using last fifty years’ wealth of knowledge and experience, such integrated electronic system development and deployment is a monumental engineering challenge. From that perspective, redesigning CMOS electronics might seem to be an overly ambitious goal specially, if that means transformation of such physically rigid complex electronic systems into a fully flexible one. To address this intriguing challenge, we have developed a unique coin like architecture based soft singular platform, which can be used as the building block of standalone fully flexible CMOS electronic system with all the aforementioned characteristics. We have devised an effective heterogeneous integration strategy based on mature and reliable CMOS technology only to integrate hybrid materials and diverse set of devices for multi-disciplinary applications. These will be the focus of this talk.

The fourth wave: ultrawide bandgap compound semiconductors for photonics and electronics

Xiaohang Li, Assistant Professor, Electrical and Computer Engineering

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KAUST

Wide bandgap (WBG) compound semiconductors including GaN have shown enormous success in solid-state lighting, display, and electrification in recent decades due to superior properties such as direct bandgap, high electron mobility, and large breakdown field. They have been changing the world by elevating living standards and addressing grand challenges such as global warming. The pioneering researchers have been recognized by numerous accolades including the Nobel Prize and most recently, the Queen Elizabeth Prize. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) compound semiconductors with bandgap larger than 3.4 eV have attracted increasing attentions: they have been regarded as the 4th wave/generation after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, respectively, both crucial for human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. However, extensive multi-disciplinary studies of growth, materials, physics, and devices are essential to unearth the potentials due to the infancy. This seminar would cover the latest research on those aspects. It includes growth of state-of-the-art materials, discovery of unique material properties, and development of a widely adopted device physics framework for photonics and electronics especially short and long wavelength photonic devices.

Topologically Transformed Electronics for Internet-of-Things Applications

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KAUST

We live in the age of information where electronics play a critical role in our daily life. Moore’s Law: performance over cost has inspired innovation in complementary metal oxide semiconductor (CMOS) technology and enabled high performance, ultra-scaled CMOS electronics.

Amal Al-Amri wins Silver Medal at Ibtikar Exibition 2013

1 min read ·
Amal Al-Amri, a Saudi female PhD student of Electrical Engineering Program in the Integrated Nanotechnology Lab led by Prof. Muhammad Mustafa Hussain has received the Silver Medal in Ibtikar Exhibition 2013.

Graphene is taking charge

1 min read ·
Graphene-based capacitors prove useful in advanced non-linear electronics.

Nano therapeutics heat up

1 min read ·
Smart, stretchable pads open up a new approach for thermotherapy.

Sensors for a light touch

1 min read ·
Biocompatible tactile sensors based on magnetic hair-like structures enable new applications for touch sensors.

Welcome to our upcoming visitors

1 min read ·
We would like to welcome to Prof. Ioannis Kymissis and Lawrence L. Wald to the King Abdullah University of Science and Technology.

DIY Challenge 2017 winner announcement

1 min read ·
Congratulations to Daniel Hickox-Young, Luke Prestowitz of Northwestern University, and Ma Yukun of National University Singapore. The three are the proud winners of the DIY Electronics Innovation Challenge 2017 that was hosted by the KAUST-NSF Research Conference on Interactive Electronics.

VSRP intern Muwei Zhang's first-author paper hits the headline of the Physica Status Solidi Journal

1 min read ·
Professor Xiaohang Li's Visiting Student Research Internship Program (VSRP) intern, Muwei Zhang has recently published his first-author paper "Structural and electronic properties of wurtzite BxAl1_xN from first-principles calculations" on the Physica Status Solidi (b) Journal.

Semiconductors with an aligned interface

1 min read ·
Efficiency gains come from tuning the properties of semiconducting materials by combining layers of different composition.

Carving out circuits that click into place

1 min read ·
An easy and reliable assembly approach, inspired by building blocks, challenges the current fabrication of electronic systems.

Making waves for ultrahigh definition displays

1 min read ·
Wavy transistors that vertically gain width without increasing their on-chip footprint could drive future flexible displays.

CEMSE's Alumni: Joanna Nassar

1 min read ·
Joanna Nassar received her Ph.D. in electrical engineering under the supervision of Professor Muhammad Mustafa Hussain in December 2017. Nassar, a Lebanese native, feels her love of creating art through her drawings and music is a major influence in how she reshapes electronics in the laboratory.

Alumni Focus: Jhonathan Prieto Rojas, M.S. 2010, Ph.D. 2014 - electrical engineering

1 min read ·
Jhonathan Prieto Rojas joined KAUST in 2009 from the National University of Colombia in Bogota and completed both his master's degree (2010) and Ph.D. (2014) in electrical engineering at the University under the supervision of Professor Muhammad Mustafa Hussain. Prieto Rojas spent six years at KAUST before leaving the University in August of 2015 for his current role as assistant professor in the electrical engineering department at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran.

KAUST partners with McLaren Racing on R&D

1 min read ·
McLaren Racing and KAUST signed a five-year research and development (R&D) agreement focused on extreme performance technology. The partnership aims to advance research in the areas of computational fluid dynamics (CFD), machine learning, fuels and lubricants, advanced mathematics and sensors and electronics. "This partnership underlines our commitment to supporting global STEM advancements," said Jonathan Neale, chief operations officer of McLaren Group. "KAUST is a leading research and education institution in these fields and mirrors our core values. McLaren Racing is part of a world

Meet KAUST prospective student: Adilya Bakambekova

1 min read ·
She loves watching films not for the plot, but in order to rethink her beliefs. She is 22, she graduates from the School of Engineering in the capital. Adilya Bakambekova may seem ordinary: she loves walking in the park, powerlifting, taking care of her five-year-old wiener dog.

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Moutazbellah Khater

Research Interests Moutaz is an experienced research and development specialist with hands-on industrial experience. His professional experience covers the full product development cycle, from the conceptual phase all the way to implementation. Throughout his academic research and professional career, He has developed several novel applications and solved numerous technical challenges and as a result he has gained a broad experience particularly in automotive electronics, power electronics, magnetic measurements, electronics manufacturing and research and development. With his unique

Jhonathan Prieto Rojas

Jhonathan Prieto Rojas joined KAUST in 2009 from the National University of Colombia in Bogota and completed both his master's degree (2010) and Ph.D. (2014) in electrical engineering at the University under the supervision of Professor Muhammad Mustafa Hussain. Prieto Rojas spent six years at KAUST before leaving the University in August of 2015 for his current role as assistant professor in the electrical engineering department at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran. Jhonathan possesses natural talent of process integration. His extensive experience includes

Husam N. Alshareef

Professor Husam Alshareef leads research in nanomaterials for energy storage and electronics.

Galo Torres Sevilla

Galo Torres Sevilla obtained both, his MS Degree and Phd Degree in Electrical Engineering in the Computer, Electrical and Mathematical Science & Engineering division of King Abdullah University of Science and Technology. He was a member of the Futuristic Electronics and Integrated Nanotechnology Lab and his Thesis was supervised by Prof. Muhammad Mustafa Hussain. Research Interest Galo's research interests include Flex-Si platform to demonstrate energy harvesting devices and nano-structured transistors for high-performance logic operation. Awards and Distinctions Graduate Fellowship, KAUST

Amal M. AlAmri

Amal Al-Amri is a Ph.D. candidate in Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jr-Hau-He. Amal received her B.Sc. in Physics from King Abdulaziz University in Saudi Arabia and proceeded to obtain her M.Sc. with a concentration in Nanomaterial's Science in Physics from King Abdulaziz University. As an active member of the Saudi Association for Physical Sciences and a member of the Talented Center King Abdulaziz University, she was able to win the Innovative Award at King Abdulaziz University in 2008 and got the second

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).

Abdullah Ali Ayed Nahi

Online intern at King Abdullah University of Science and Technology(KAUST)..electrical engineering student at King Fahd University of Petroleum and Minerals KFUPM .
<> optoelectronics (28)

InGaN-based light-emitting devices

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B9 L2 H1

III-Nitride-based light-emitting diodes (LEDs) are already commercialized for instance blue and green LEDs. It is very contributed as energy saving light source all over the world. This material is a very attractive material, capable of the emitting range is not only blue light but also UV and visible light. Moreover, application as a power device is also possible, it is one of the materials considered to lead the energy saving society in the future. Visible light LED has a wide range of applications. Visible light LED has a wide range of applications. we expect that there is some application, for instance, μ-LED display, optical communication, plant cultivation, medical treatment, and so on. In the seminar, I will talk about III-Nitride-based visible light emitting devices and introduce recent research with outstanding metalorganic chemical vapor deposition (MOCVD) growth technique.

Study of ultraviolet AlGaN nanowires light-emitting diodes

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B3 L5 R5209

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Study of III-nitride Nanowire Growth and Devices on Unconventional Substrates

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B3 L5 R5209

This thesis aims to investigate the microscopic characteristics of the nanowires and expand on the possibility of using transparent amorphous substrate for III-nitride nanowire devices. In this work, we performed material growth, characterization, and device fabrication of III-nitride nanowires grown using molecular beam epitaxy on unconventional substrates including silicon substrates and fused silica substrates. We also investigated the effect of various nucleation layers on the morphology and quality of the nanowires.

Hybrid integration of materials for ultraviolet detection

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B9 L2 H1 R2322

Semiconductors are pervasive in consumer electronics and optoelectronics, and the related optical devices are deemed disruptive that Nobel Prize in Physics in 2014 was awarded to the inventors of blue light-emitting diodes (LEDs), which “has enabled bright and energy-saving white light sources”. While AlInGaN-based lasers and LEDs, and silicon-based photodetectors are currently matured, unconventional usage based on the materials has demonstrated their further potential, including solar-hydrogen generation, indoor-horticulture, and high-speed communication.

Deep-Ultraviolet Optoelectronic Devices Enabled by the Hybrid Integration of Next-Generation Semiconductors and Emerging Device Platforms

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B3 L5 R5209

In this dissertation, the design and fabrication of deep-ultraviolet photodetectors, based on gallium oxide and its alloys, through the heterogeneous integration with metallic and other inorganic materials is investigated. The crystallographic properties of grown oxide films formed directly and indirectly on silicon, magnesium oxide, and sapphire are examined, and the challenges that hinder the realization of efficient and reliable deep-ultraviolet photodetectors are elaborated on. I provide an overview of aluminum nitride, gallium oxide, sapphire, and silicon substrates as platforms for deep-ultraviolet optoelectronic devices, in which I elaborate on the challenges associated with using sapphire as a platform for efficient deep-ultraviolet devices and detail advancements in device growth and fabrication on silicon and magnesium oxide substrates.

CEMSE Welcomes new Professor: Dr. Jr-Hau

1 min read ·
Prof. He joins from National Taiwan University, where he was a tenured associate professor of Electrical Engineering.

Nanoscale patterning is better with gas

1 min read ·
Adding a gas enhances the processing of hybrid organic-inorganic materials for improved solar cells.

Visiting Ph.D. Student Meng-Lin Tsai and Prof Jr-Hau He device the thinnest highly-efficient solar cell

1 min read ·
At King Abdullah University of Science and Technology (KAUST), a team of researchers developed what might be the effective solution to the problem: the thinnest solar cells with lateral geometry offering high optoelectronic performances while reducing production costs of extended photovoltaic architecture. The research was recently published in Advanced Material and reported by Nature Middle East.

Jr-Hau He elected SPIE Fellow

1 min read ·
Jr-Hau He, KAUST associate professor of electrical engineering, has recently been elected as an SPIE Fellow for his achievements in solar cells, photodetectors and semiconductor optics research. SPIE, the international society for optics and photonics, annually recognizes society members who have made significant scientific and technical contributions in the multidisciplinary fields of optics, photonics and imaging, and for their outstanding technical contributions and service to SPIE. He and 72 additional fellows of the society will be promoted later this year.

KAUST Associate Professor Jr-Hau He wins Nano Energy Award 2019

1 min read ·
The Beijing Institute of Nanoenergy and Nanosystems of the Chinese Academy of Sciences recently announced that KAUST Associate Professor Jr-Hau He is one of three selected scholars who will receive the Nano Energy Awards 2019.

Yue Wang

Yue Wang's research focuses on semiconductor optoelectronics, high-speed color-converting luminescent devices, and optical wireless communication.

Xiaohang Li

Professor Li is a leading scholar in the research of (ultra)wide bandgap semiconductors and high density stacked transistor IC that are bringing revolutionary impacts on the optoelectronic and electronic industries.

Rongyu Lin

Rongyu Lin is a Ph.D. student at the Advanced Semiconductors Laboratory under the supervision of Prof. Xiaohang Li at King Abdullah University of Science and Technology (KAUST). He received his Bachelor of Science in Physics from the Southern University of Science and Technology. Research Interest Rongyu's research interests include 2D materials characterization, device fabrication, and Optoelectronics.

Redha Al Ibrahim

Postdoctoral Research Fellow, Electrical and Computer Engineering
Ph.D student at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) at King Abdullah University of Science & Technology (KAUST). Education Profile January 2020 - Present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia August 2018 - December 2019, M.S. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia August 2014 - May 2018, B.S in Optics at University of Rochester, Rochester, NY, USA Professional Memberships ​Member of OSA (Optical Society of America) Member of SPIE (International Society for Optics and Photonics)

Omar Alkhazragi

Introduction Omar Alkhazragi received the degree of Bachelor of Science in Electrical Engineering in 2018 from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, and a master’s degree in electrophysics in 2019 from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. He is now an electrophysics Ph.D. student in the Photonics Laboratory at KAUST. The primary focus of his research is on optoelectronic devices fabrication and applications, including optical and quantum communication. Research Interests Photonics and optoelectronics Semiconductor

Osman M. Bakr

Osman M. Bakr is a professor of Material Science and Engineering at KAUST, specializing in the physics and chemistry of nanomaterials with unique optical, electronic, and magnetic properties. His research focuses on the development of hybrid nanomaterials for solar cells, batteries, and photonic devices.

Mohammed Abdul Majid

Postdoctoral Research Fellow, Photonics Laboratory
Mohammed Abdul Majid was a Postdoctoral Fellow at Photonics Laboratory at King Abdullah University of Science and Technology (KAUST), where he worked on applying III/V semiconductor quantum dot/quantum well devices to applications such as biomedical imaging, optical communications to solid-state lighting. Mohammed received his M.Sc. degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia in 2002. In 2011, he obtained his Ph.D. degree in Electronic and Electrical Engineering from the University of Sheffield in the U.K. From 2002-2008

Mohammad Khaled Shakfa

Postdoctoral Research Fellow, Photonics Laboratory
Mohammad Khaled Shakfa was a Postdoctoral Fellow at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). Previously, he was a Postdoctoral Research Fellow in the workgroup of Prof. Dr. Martin Koch, Experimental Semiconductor Physics at Philipp University of Marburg (Marburg, Germany). He received his Ph.D. in Electrical Engineering at Braunschweig University of Technology (Braunschweig, Germany) in 2015. His Ph.D. research focused on the investigation of localization effects

Mohamed Ebaid

Postdoctoral Research Fellow, Photonics Laboratory
Currently works at ​Lawrence Berkeley National Lab, California, USA

Latifah Al Maghrabi

Latifah Al-Maghrabi is pursuing a master's degree in Electrical Engineering in the electrophysics track at King Abdullah University of Science and Technology (KAUST). Currently, the questions that guide Latifah's journey of investigation revolve around exploring novel lower dimensional materials for integration with group-III-nitride-based optoelectronic devices at the Photonics Laboratory at KAUST. By understanding the physics at the device level, she hopes to be able to enhance optoelectronics used for light and energy generation. In May 2018, Latifah graduated with a B.S. in Electrical

Kuang-Hui Li

Kuang-Hui Li joined Photonics Laboratory at King Abdullah University of Science and Technology (KAUST) in the Fall of 2018. He obtained two bachelor degrees (2011) in Electro-Physics and Applied-Math from National Chiao Tung University, Taiwan. Research Interest Kuang's research interests include Semiconductor epitaxy by MBE, MOCVD, and PLD, Material characterization, e.g. HRXRD, TEM/STEM, RBS, Nanofabrication processes, Semiconductor optoelectronics devices (LED, Photodetector, Laser). His current research focuses on III-oxide material and heterostructures for device applications such as UV

Huafan Zhang

Huafan Zhang is currently a Ph.D. Student in the Photonics Laboratory. She obtained her BEng. (2017) in Electronic science and technology from the University of Electronic Science and Technology of China (UESTC), Chengdu, China. She joined King Abdullah University of Science and Technology (KAUST) in the Fall of 2017 and achieved MSc in Electrical Engineering at KAUST in 2018. Her current research focuses on III-Nitride nanowire-based photoelectrochemical water splitting, radiative cooling for optoelectronic devices and materials for water collections. Research Interests Her research interests

Hadi Amata

Hadi Amata is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Amata received his master degree in Electronics/Microelectronics from Provence University in France in 2008. He obtained a doctoral in Electrical Engineering “Optics and Photonics” from Jean Monnet University of Saint Etienne in France in 2012. He started his career life as a teaching assistant at Saint-Étienne Institute of Technology (IUT) in 2010. He was also a teaching

Edgars Stegenburgs

Edgars Stegenburgs joined the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division of KAUST as an MS/Ph.D. student and graduate researcher at Photonics Laboratory in January 2015. Previously he was working in the Laser Centre of the University of Latvia as a natural sciences laboratory assistant under the supervision of Dr. Aigars Ekers in the Laser-manipulation laboratory. There he developed his bachelor thesis "Formation of bright and dark states in non-degenerate quantum systems and ultra-high vacuum system design for Rb atoms magneto-optical trap" and earned the

Daisuke Iida

Senior Research Scientist, Electrical and Computer Engineering
Daisuke Iida is a Senior Research Scientist at Energy Conversion Devices and Materials (ECO Devices) Laboratory in the Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE). He holds a Ph.D. in Materials Science and Engineering from Meijo University in 2011. Afterward, he held postdoctoral and visiting researcher positions at Meijo University and Technical University of Denmark. Then, he worked as an assistant professor at Tokyo University of Science from 2014 to 2017. His research interest is III-nitride semiconductors-based optoelectronic devices, especially the

Davide Priante

Davide Priante obtained his Ph.D. and Master's degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). Davide received his B.Sc. degree in Material Science Engineering from the University of Padova, Italy, in 2013. He joined KAUST in February 2014 as an M.S. to a Ph.D. student in Photonics Laboratory in the Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) Division. He received his MSc degree in August 2015 with a thesis based on the recombination mechanism of CH3NH3PbBr3 perovskite. Davide

Chao Shen

Chao Shen received his Ph.D. degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). Currently, Chao is working as a Consultant at KAUST, with KAUST Innovation and Economic Development. Research Interests Chao's research interests included GaN optoelectronics, III-nitride​ diode lasers, Photonic integrated circuits, and Laser-based solid-state lighting, visible-light communications, III-nitride photonic integrated circuit, GaN-based laser diodes, superluminescent diodes (SLDs), micro-LEDs, and VCSELs, laser

Carmen Leticia Castrejon Barron

Carmen Leticia Castrejon Barron is a Ph.D. candidate at Energy Conversion Devices and Materials (ECO Devices) Laboratory under the supervision of Professor Kazuhiro Ohkawa at King Abdullah University of Science and Technology (KAUST). Education MSc, Electrical Engineering, KAUST, Saudi Arabia, 2016 ME, Education, Tec Milenio university, Chihuahua, Mexico, 2013 BSc, Electromechanical Engineering, Instituto Tecnologico de Ciudad Jimenez, 2009 Research Interests Carmen's research interests include Light sources, Solar Energy, Plant biology and Desert agriculture.
<> Partial Differential Equations (28)

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

Part 3: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems.

Part 1: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 1: I will show how to transform suitable mean-field game (MFG) systems into infinite-dimensional convex optimization problems. Furthermore, I will present Uzawa’s algorithm and augmented Lagrangian approach for solving convex optimization problems. Finally, I will demonstrate how to apply these methods to approximate solutions of corresponding MFG systems.

Estimation Problems for Partial and Fractional Differential Equations - Spring 2019

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B9 L2 H1

We will present some new methods for source and parameters estimation for partial and fractional differential equations and illustrate the results with some simulations and real applications.

Part 2: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 2: I will present new Fourier approximation techniques for nonlocal MFG systems and discuss how these techniques fit the variational framework presented on Day 1. Then I will introduce the Chambolle-Pock primal-dual hybrid gradient optimization method and apply a variant of this method to approximate nonlocal MFG models via Fourier approximation techniques above.

Part 3: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 3: I will present Fuglede’s proof of the quantitative isoperimetric inequality for convex and for nearly spherical sets

Part 1: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 1: I will introduce some basic notions of the theory of sets of ignite perimeter and of geometric measure theory.

Part 1: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Part 2: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 2: I will present De Giorgi’s proof of the isoperimetric inequality and comment on other proofs of the isoperimetric inequality

Part 4: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 4: I will discuss the quantitative isoperimetric inequality in the general case and the quantitative form of related geometric and functional inequalities.

Part 2: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Part 3: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

Part 1B | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

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KAUST

PDE Backstepping Boundary Control of Parabolic PDEs Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

Part 1A | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

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KAUST

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

Part 2B | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

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KAUST

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

Part 2A | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

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KAUST

Abstract Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite

On the collapse mechanism of Wave Maps

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B1 L4 R4214

The Wave Map system describes the evolution of waves constrained on a (Riemannian) manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.

Aggregation-Diffusion and Kinetic PDEs for collective behavior: applications in the sciences

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B1 L3 R3119

In this seminar, I will present a survey of micro, meso and macroscopic models where repulsion and attraction effects are included through pairwise potentials. I will discuss their interesting mathematical features and applications in mathematical biology and engineering. Qualitative properties of local minimizers of the interaction energies are crucial in order to understand these complex behaviors. I will showcase the breadth of possible applications with three different phenomena in applications: segregation, phase transitions, and consensus.

Tensor-train Methods for Partial Differential Equations and its application to a Neutron Transport Problem

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B9 L4 R4225

Tensor network techniques are known for their ability to approximate low-rank structures and beat the curse of dimensionality. They are also increasingly acknowledged as fundamental mathematical tools for efficiently solving high-dimensional Partial Differential Equations (PDEs). In this talk, we present a novel method that incorporates the Tensor Train (TT) and Quantized Tensor Train (QTT) formats for the computational resolution of time-independent Boltzmann Neutron Transport equations (BNTEs) in Cartesian coordinates.

KAUST mathematician named Fellow to European Academy of Sciences

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, is named a 2020 Fellow to the European Academy of Sciences (EurASc) in recognition of his outstanding scholarship in the field of mathematical and numerical analysis of partial differential equations.

Meet KAUST prospective student: Stefanos Georgiadis

1 min read ·
Stefanos Georgiadis is an applied mathematics graduate who will join KAUST from the School of Applied Mathematical and Physical Sciences, Athens, Greece. Stefanos will join KAUST this fall as a Ph.D. candidate and member of the Applied Partial Differential Equations (AppliedPDE) research group under the supervision of Professor Athanasios Tzavaras.

Stefanos Georgiadis

Stefanos Georgiadis is a Ph.D. candidate in a co-tutelle between KAUST and TU Wien, under the supervision of Professor Athanasios Tzavaras and Ansgar Jüngel. Before joining KAUST, Stefanos obtained his bachelor and master's degree in applied mathematics from the School of Applied Mathematical and Physical Sciences of the National Technical University of Athens in Greece. Research Interests Stefanos' research interests include the analysis of nonlinear partial differential equations as they apply to the modeling of multicomponent systems of gases.

Nuno J. Alves

Short Bio Mr. Nuno Alves is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He has got bachelor and master's degrees from Instituto Superior Tecnico, University of Lisbon, Portugal. Mr. Nuno had been a visiting student at KAUST from March 2014 to July 2014. Moreover, during the academic year 2017/2018 he was working as an invited teaching assistant at Polytechnic Institute of Beja, Portugal. His work is essentially related with non-linear partial

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research

David Keyes

Senior Associate to the President, King Abdullah University of Science and Technology
David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.

Athanasios Tzavaras

KAUST Professor of Applied Mathematics and Computational Science Athanasios (Thanos) Tzavaras specializes in mathematical modelling, analysis and computation of partial differential equations pertaining to fluid mechanics or modeling of materials.
<> Biosensors (27)
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

A voice to the voiceless: Montserrat Ramirez De Angel takes “Best Pitch” award at IEEE 2024 Sensors Conference

3 min read ·
Montserrat Ramirez de Angel wins the Best Pitch Award at IEEE 2024 Sensors Conference for her innovative work in biosensors and sensor technology.

KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

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KAUST

The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together.

Biosensors challenges and opportunities

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KAUST

No summary is available.

BioChips: Leveraging Integrated Electronics for Sensing, Medicine, and Science

Pamela Abshire, Professor, Department of Electrical and Computer Engineering and Institute for Systems Research at the University of Maryland, College Park

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B9 L2 R2322

This talk will provide a brief overview of LoCMOS systems, the technologies used to construct them, and their application to novel applications in biosensing, medical diagnostics, and neuroscience. The integration of integrated circuits into LoCMOS devices poses a number of distinct and vexing challenges, increasing complexity while reducing the need for external instrumentation.

Analysis and verification of identical-order mixed-matrix fractional-order capacitor networks

1 min read ·
Kartci, Aslihan, et al"Analysis and verification of identical-order mixed-matrix fractional-order capacitor networks." In 2018 14th Conference on Ph. D. Research in Microelectronics and Electronics (PRIME), 2018, 277. In the open literature while capacitors are introduced with -90 degrees phase angle, here we described our fabricated polymer composite, mixed matrix, as a fractional- order capacitor (FoC). The effect on phase and pseudo- capacitance using a detailed numerical and experimental study of circuit network connections of three identical-order FoCs is shown. The used devices have

Detecting metabolites at close range

1 min read ·
Pairing a conjugated polymer with a redox enzyme generates a fast, selective and sensitive electrochemical biosensor for metabolites.

Magneto-Acoustic Resonator for Aquatic Animal Tracking

1 min read ·
Abdullah S. Almansouri, et al., "Magneto-Acoustic Resonator for Aquatic Animal Tracking" IEEE Transactions on Magnetics 55 (2), 2019, 1. Over the past three decades, passive acoustic telemetry has significantly helped marine scientist to study and understand the spatial ecology, migratory behaviors, and mortality rates of aquatic animals. A popular telemetry system consists of two components: an acoustic transmitter tag attached to an aquatic animal and powered by a small battery, and a stationary station that receives the acoustic signals from the tagged animal and determines its location

KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

1 min read ·
The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” was held in KAUST between the 25th and 27th of February. This yearly event aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together, including Chemistry, Electrical Engineering, Biology, and Material Science, who are at the top of their

Meet KAUST prospective student: Carlos Augusto Zan Malaguti

1 min read ·
Carlos Augusto Zan Malaguti, 23, from Brazil, obtained his bachelor’s degree in electronics and telecommunications engineering from Sao Paulo State University (UNESP). Carlos will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Sensors Lab under the supervision of Professor Khaled Nabil Salama.

Coronavirus testing made quick and easy

1 min read ·
New diagnostic technique could help contain the spread of COVID-19 and other viral outbreaks.

Plant probes make sense for sustainable farming

1 min read ·
Ultrathin needles for probing plants could help keep crop health in check.

KAUST New Faculty: Yating Wan, Assistant Professor of Electrical and Computer Engineering

1 min read ·
Yating Wan, assistant professor of electrical and computer engineering, joins KAUST as a member of the KAUST Photonics Laboratory from her most recent position as a postdoctoral research associate at the University of California, Santa Barbara (UCSB), U.S.

Yuvaraj Sivalingam

Yuvaraj Sivalingam is a Research Scientist at the Sensors Lab, Computer, Electrical, and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. Education and Early Career He received his M.Sc. in Physics in 2007 from Madurai Kamaraj University, India and in 2009 he received his M.Tech. in Sensor System Technology from VIT University (India), with a project entitled “Studies on the development of compositions for hydrogen sensing applications and their relevant instrumentation’. In 2008-2009 during his M.Tech.
Yating Wan

Yating Wan

Dr. Yating Wan, an assistant professor of electrical and computer engineering at KAUST, excels in silicon photonics, with special emphasis on the integration of on-chip light sources.

Tutku Beduk

Tutku Beduk is currently a PhD student at the Sensors Lab, in the Material Science and Engineering Department, MSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Tutku Bedük received her bachelor degree from Middle East Technical University in Chemistry with a High Honor Degree, Turkey in 2016. She also competed her MSc degree in Bilkent University in the field of Chemistry, Turkey in 2018. She is currently a PhD candidate in the Sensor Lab Group under the direction of Prof. Khaled Nabil Salama at King Abdullah University of Science and Technology

Sumeyra Agambayev

Sumeyra Agambayev worked as a Researcher at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sumeyra's research interests included BioSensors, Microfluidics, ​Inkjet printing.

Sandeep Surya

Postdoctoral Research Fellow, Sensors
Dr. Sandeep Surya is currently a Post-Doctoral fellow at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Sandeep G. Surya received his Ph.D. degree from IIT Bombay in 2017. He is currently a Post-Doctoral Fellow with Sensors Lab, CEMSE Division, King Abdullah University of Science and Technology. Prior to this, he was a Research Associate with the Microsystems Technology Research Unit, Fondazione Bruno Kessler, Trento, Italy. His forte is in building platforms for different sensor

Sanjit Majhi

Postdoctoral Research Fellow, Sensors
Sanjit Manohar Majhi has recently joined the Sensors group, as a Postdoctoral fellow. Dr. Majhi currently focused on the synthesis of nanomaterials based on 1D/2D metal oxides for gas and biosensing applications. Education and early career Sanjit Manohar Majhi received his Ph.D. degree from the department of Electronic Information Materials Engineering in Chonbuk National University, South Korea in August 2017. He received his master degree in Chemistry from Utkal University, Bhubaneswar in India in 2008. He joined recently in King Abdullah University of Science and Technology, to work under

Sakandar Rauf

Education and Early Career Dr. Sakandar Rauf received his Ph.D (Field of Study: Biosensors) from the University of Glasgow, U.K. in 2010. He obtained an MPhil degree in Micro- and Nanotechnology Enterprise from the University of Cambridge, U.K in 2005 and MPhil in Biotechnology from the National Institute for Biotechnology and Genetic Engineering (NIBGE), Pakistan in 2004. Dr. Rauf studied M.Sc. in Chemistry from Islamia University Bahawalpur, Pakistan in 2001. He received Cambridge Commonwealth & DFID shared Scholarship from the University of Cambridge, Dorothy Hodgkin’s Postgraduate Award

Renad Bougis

Research Interests Wireless sensors design Biosensors and bioelectronics Education ​Bachelors of Science in Electrical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA (2016-2020)​

Hamzeh Abu Qamar

MS Student, Electrical, Electronics and Communications Engineering at King Abdullah University of Science and Technology (KAUST).

Eloise Bihar

Postdoctoral Research Fellow, Sensors
Eloise is a postdoctoral fellow specialized in the field of Printed electronics for biomedical applications. Education and early career Eloise received her M.Sc. in engineering, from Grenoble INP-Pagora, in Grenoble, France, in 2010. She later received her PhD. in bioelectronics from Ecole des Mines de Saint Etienne in France in 2016 under the guidance of Prof. George Malliaras. She is currently working in Prof. Khaled Salama’s group, in collaboration with the groups of Prof. Inal and Prof. Baran. Research Interests Eloise’s current research focuses on the fabrication of printed autonomous

David Lago Cachón

Postdoctoral Research Fellow, Communication and Computing Systems Lab
Biography David Lago Cachón received his B.Sc. in Physics, specialization in Electronic, Automatism and Signal Processing in 2008 and his M.Sc. in Science and Technology of Materials in 2010, both from University of Oviedo. He obtained his PhD in Applied Physics in 2016, with qualification Excellent cum laude and proposed to Extraordinary doctorate award. At the same time, he has been highly implicated in Science Communication, given talks in schools, pubs and even a TEDx. Also, he has been interviewed and consulted by local and regional media. With some friends he created an astronomy club

Carlos Augusto Zan Malaguti

Carlos Augusto Zan Malaguti is an M.S./Ph.D. candidate in the KAUST Sensors Lab under the supervision of Professor Khaled Nabil Salama. Education and Early Career Carlos received his bachelor’s degree in Electronics and Telecommunications Engineering from Sao Paulo State University (UNESP), Brazil, in 2020. During his undergrad, Carlos’ work focused on performance research through electrical characterization of 0.13 um SOI MOSFET devices. In the Summer/Fall of 2019, Carlos was a Visiting Student at KAUST, under the supervision of Professor Muhammad M. Hussain. He worked on nanofabrication

Aljawharah Alsharif

Aljawharah Alsharif is a PhD student in Electrical and Computer Engineering and a member of the Smart, Advanced Memory Devices and Applications lab (SAMA) at King Abdullah University of Science and Technology, Saudi Arabia. She is passionate about shaping the future of wearable devices, flexible and soft electronics, sensors, and actuators through 4D/3D printing.

Abdullah Bukhamsin

Abdullah Bukhamsin is a Ph.D. student at the Sensors Lab, in the Bioengineering Department, under the supervision of Professor Khaled Nabil Salama at KAUST. Prior to this, Abdullah obtained an M.S. Degree in Engineering under the supervision of Professor Jurgen Kosel at King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdullah Bukhamsin received his B.Sc. degree in Bioengineering from Rice University (Houston, Texas) in 2018. Following that, he completed his M.Sc. degree in Electrical Engineering from King Abdullah University of Science and Technology

Abdulrahman Aljedaibi

Electrical Engineering PhD student at CEMSE, King Abdullah University of Science and Technology (KAUST).
<> RobotoKAUST (27)

Human Robot Interaction with Natural Language Logic: a Step Closer

Emmanuel Roche, PhD, CEO of Clover.AI

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KAUST

Featured Sessions
For more information please visit the website

Safe learning and control with L1 adaptation

Naira Hovakimyan, Professor, Mechanical Science and Engineering, University of Illinois

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KAUST

Featured Sessions
For more information please visit the website

Dynamic Explorations of a TiltWing Aircraft Transitioning from Conventional to Hovering Flight

John Hauser, Professor, University of Colorado Boulder

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KAUST

Featured Sessions
For more information please visit the website

Distributed AI: Optimal algorithms for distributed stochastic non-convex optimization

Usman A. Khan, Associate Professor, Tufts University

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KAUST

Featured Sessions
For more information please visit the website

Trajectory planning in uncertain transient currents: a stochastic optimization approach

Ricardo M. Lima, Research Scientist, CEMSE, KAUST

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KAUST

Featured Sessions
For more information please visit the website

The challenging path to creating fully autonomous and controllable microrobots

Prof. Néstor O. Pérez-Arancibia, Autonomous Microrobotic Systems Laboratory, University of Southern California (USC)

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KAUST

Featured Sessions
For more information please visit the website

Core Challenges in Navigation in Human Environments

Jean Oh, Senior Systems Scientist, Robotics Institute, Carnegie Mellon University

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KAUST

Featured Sessions
For more information please visit the website

Enhancing visibility for reliable underwater visual SLAM

Ayoung Kim, Associate Professor, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)

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KAUST

Featured Sessions
For more information please visit the website

Levering AI for Unmanned Aerial Systems: Deployment Strategies

Anis Koubaa, Professor, Prince Sultan University

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KAUST

Featured Sessions
For more information please visit the website

Using drones, fluid lensing, and network analysis to get a more comprehensive view of coral reef mapping in the Red Sea

Colleen Campbell, PhD Student, Red Sea Research Center, KAUST

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KAUST

Featured Sessions
For more information please visit the website

Autonomous Vision-Based Navigation and Control for Robotic Space Objects

Hesham Shageer, Assistant Professor, King Abdulaziz City for Science and Technology KACST

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KAUST

Featured Sessions
For more information please visit the website

Multi-Robot Task Allocation Games in Dynamically Changing Environments with Application to Trash Collection

Shinkyu Park, Assistant Professor, Electrical and Computer Engineering, KAUST

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KAUST

Featured Sessions
For more information please visit the website

From one to many, coordination problems of multiple mobile robots

Lucia Pallottino, Associate Professor, University of Pisa

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KAUST

Featured Sessions
For more information please visit the website

Safe, Interaction-Aware Decision Making and Control for Robot Autonomy

Marco Pavone, Associate Professor, Aeronautics and Astronautics, Stanford University

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KAUST

Featured Sessions
For more information please visit the website

The Robots are Coming – to your Farm

Girish Chowdhary, Associate Professor, UIUC

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KAUST

Featured Sessions
For more information please visit the website

Swarms for People

Sabine Hauert, Associate Professor, Bristol Robotics Laboratory, University of Bristol

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KAUST

Featured Sessions
For more information please visit the website

An Egocentric Wearable Camera for Assistive Technologies

Mohammed Kutbi, Assistant Professor, Saudi Electronic University

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KAUST

Featured Sessions
For more information please visit the website

Xwing: the journey to deploying autonomous cargo aircraft

Maxime Gariel, Chief Technology Officer, Xwing

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KAUST

Featured Sessions
For more information please visit the website

Optimization-Based Control of Autonomous Aerospace Systems

Behcet Acikmese, Professor, William E. Boeing Department of Aeronautics and Astronautics, University of Washington, USA

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KAUST

Featured Sessions
For more information please visit the website

Safe autonomy with imperfect information

Necmiye Ozay, Associate Professor, University of Michigan

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KAUST

Featured Sessions
For more information please visit the website

Efficient Reachability for Safe Autonomous Systems: A Mixed Monotone Approach

Sam Coogan, Assistant Professor, Georgia Institute of Technology

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KAUST

Featured Sessions
For more information please visit the website

On the Fundamental Challenges for Self-Driving Cars

Emilio Frazzoli, Professor, Dynamic Systems and Control, ETH Zürich

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KAUST

Featured Sessions
For more information please visit the website

Certification of Autonomous Vehicles: A Regulator-Oriented Approach

Huan Xu, Assistant Professor, University of Maryland

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KAUST

Featured Sessions
For more information please visit the website

Designing And Modelling Soft Active Artificial Muscles “Geometric and Material Nonlinearities”

Marwa Eldiwiny, PhD Student, IEEE RAS Org

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KAUST

Featured Sessions
For more information please visit the website

RobotoKAUST 2022 Call for Poster Abstracts

Prof. Eric Feron, Electrical and Computer Engineering

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KAUST

We are excited to announce the KAUST Research Conference on Robotics and Autonomy 2022, which will be held on Feb 28 - March 2, 2022 (#RobotoKAUST). The conference will address the most recent trends of robotics application in a range of disciplines. The conference will give space for presentations and discussions of systems, items, and methods related to robotics and autonomy. As part of this event, we are accepting poster abstract submissions.

KAUST Research Conference of Robotics and Autonomy 2021

1 min read ·
The KAUST Research Conference on Robotics and Autonomy 2021, #RobotoKAUST, brought together leading international and in-Kingdom robotics researchers and government and industry representatives from the robotics industry to discuss robotics and its applications. The online event was organized by Prof. E. Feron, Prof. B. Ghanem, Prof. X. Zhang and had six sessions on Marine Robotics, Human-Robot Interactions, Aerospace Robotics, Mobile Robotics, Wearable Robotics & Microrobotics, Assured Autonomy. Twenty-seven speakers (seven from KSA including three from KAUST, and three from industry)

The Recordings of the #RobotoKAUST talks are available

1 min read ·
The first KAUST Research Conference on Robotics and Autonomy 2021, #RobotoKAUST, brought together leading international and in-Kingdom robotics researchers and government and industry representatives from the robotics industry to discuss robotics and its applications. The recordings of the talks are available online! To watch the talks, please visit our website #RobotoKAUST or our KAUST RISC Lab YouTube Channel.
<> data science (26)

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

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B1 L4 R4214

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Digital Health: promises and pitfalls

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B9 L3 R3223

This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.

KAUST-MBSC Invitational Healthcare Analytics and Data Science Workshop

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B3 L5 R5220

The amount of collected medical data and computational power are growing rapidly, which creates new opportunities to impact and improve healthcare by advanced analytics and data science. We aim to bring together practitioners and researchers from across the Kingdom to exchange knowledge and explore possible collaborations. To register, please, fill form here.

Transparent and Robust Causal Inference in Data Science

Carlos Cinelli, Ph.D. candidate, Department of Statistics, UCLA

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KAUST

The past few decades have witnessed rapid and unprecedented theoretical progress in the science of causal inference, ranging from the “credibility revolution” with the popularization of quasi-experimental designs, to the development of a complete solution to non-parametric identification with causal graphical models. Most of these theoretical progress, however, relies on strong, exact assumptions, such as the absence of unobserved common causes, or the absence of certain direct effects. Unfortunately, more often than not these assumptions are very hard to defend in practice. This leads to two undesirable consequences for applied quantitative work in the data-intensive sciences: (i) important research questions may be neglected, simply because they do not exactly match the requirements of current methods; or, (ii) researchers may succumb to making the required “identification assumptions” simply to justify the use of available methods, but not because these assumptions are truly believed (or understood). In this talk, I will discuss new theories, methods, and software for permitting causal inferences under more flexible and realistic settings. These tools empower scientists, and policymakers to both examine the sensitivity of causal inferences to violations of its underlying assumptions, and also to draw robust and trustworthy conclusions from settings in which traditional methods fail.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

Towards Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions

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B2 L5 R5220

Abstract The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These

Scalable Spatial Data Science for Social Scientists

Prof. Amr Magdy

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B4 L5 R5220

The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference

1 min read ·
Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.

Lifting up female scientists

1 min read ·
Women in Data Science (WiDS) is like the Global Women's March for analytics; it's a phenomenon too big for just one city. WiDS is the largest data science conference on Earth—taking place in over 50 countries, it has attained more than 100,000 attendees and is tagged as a global movement. The annual conference aims to inspire and educate data scientists worldwide—regardless of gender—and support women in the field. This year's WiDS conference was held at Stanford University with more than 100 regional institutions all over the world participating, including KAUST.

Alumni Feature: Majed Alzahrani

1 min read ·
Working in one of the largest companies in the world is a dream come true for many. For Majed Alzahrani, a former Ph.D. student who worked under the supervision of Associate Professor Xin Gao, things couldn't be better. After graduating in 2017, he quickly joined Saudi Aramco as a data scientist in its Drilling Data Science unit.

2019 Statistics and Data Science Workshop

1 min read ·
From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

Joining forces to advance healthcare analytics in the Kingdom

1 min read ·
The recent KAUST-Prince Mohammed Bin Salman College (MBSC) Invitational Healthcare Analytics and Data Science Workshop brought together top-level clinicians, healthcare executives, representatives to discuss digitization and advancement of healthcare analytics in Saudi Arabia.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Mohamed Habib Loukil

MS Degree in the Communication Theory Lab (CTL) under the supervision of Professors Mohamed-Slim Alouini and Tareq Al-Naffouri at King Abdullah University of Science and Technology (KAUST).

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Małgorzata Forystek

M.S. Student, Statistics
Statistics Master's student interested in Data Science

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering
PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Eman Abdullah Kabbas

Deeply passionate about Bayesian statistics, Eman Kabbas is a Math/Statistics Lecturer in the General Studies Department at Jubail Industrial College (JIC). Currently, Eman is a Ph.D. candidate in Applied Mathematics and Computational Sciences at KAUST, specializing in Bayesian statistics under the supervision of Professor Håvard Rue.

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in
<> Statistics of extremes (26)
Simon Nik

Simon Nik

Postdoctoral Research Fellow, Statistics
Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany, with a thesis entitled " Stochastic modeling and statistical analyses of non-negative data and times series". He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany. He joined KAUST as a postdoc in December 2024. Research interests Simon's current research interests focus primarily on extreme

Workshop on Computational Space-Time Statistics

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B1 L4 R4102

Workshop on Computational Space-Time Statistics

Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

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B5 L5 R5220

This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

Prof. Miguel de Carvalho and Rodrigo Rubio (PUC, Chile) visit extSTAT

1 min read ·
Prof. Miguel Carvalho holds a research and teaching positions at PUC Chile, EPFL, Banco de Portugal, and UNL. He is an applied mathematical statistician with a variety of research interests including, inter alia, statistical inferences for small-probability events, geometrical statistics, methods for data visualization and graphical learning, econometrics, and medical diagnostic assessment.

Prof. Benjamin Shaby (Penn State University, USA) visits extSTAT

1 min read ·
Prof. Ben Shaby is an Assistant Professor in the Statistics Department and the Institute for CyberScience at Penn State, where he started in 2013. Before that, he was a postdoc with Cari Kaufman at UC Berkeley. Previous stop was in Durham at the Statistics and Applied Mathematical Institute (SAMSI) and Duke University. He did his Ph.D. work in Statistics at Cornell University with David Ruppert and Marty Wells. Before starting at Cornell, he spent his undergrad years at Stanford (go Cardinal!). He then spent two great years working for Chris Field at the Carnegie Institution's Department of

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

New paper accepted in JABES

1 min read ·
New accepted paper: Castro-Camilo, D., Huser, R., and Rue, H. (2019+), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics, to appear [ PDF preprint]

New paper accepted in JGR - Earth Surface

1 min read ·
New accepted paper: Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019+), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research-Earth Surface [ PDF preprint]

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Zhongwei Zhang

Ph.D. Student, Statistics
Zhongwei Zhang was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Zhongwei successfully defended his PhD thesis entitled " Flexible Extremal Dependence Models for Multivariate and Spatial Extremes" on October 25th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Jonathan Tawn (external examiner from Lancaster University, UK), David Bolin, and Ajay Jasra. For his next career steps, Zhongwei has accepted a postdoctoral position at the University of

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

Xuanjie Shao

Ph.D. Student, Statistics
Xuanjie Shao is a M.S./Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Education and Early Career Xuanjie Shao obtained his Bachelor from the Southern University of Science and Technology, Shenzhen, China. He joined the M.S./Ph.D. program in Statistics at KAUST in 2020, and successfully defended his MS thesis on April 5th, 2022; see his MS thesis here. Since then, he has been working on his PhD research about spatial extremes. Research interests Xuanjie Shao's research interests focus on extreme-value

Rustam Bekishev

M.S. Student, Statistics
Rustam Bekishev obtained his MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After his time at KAUST, he got a job as a Senior Business Analyst at the Applied Economics Research Center ( AERC), Kazakhstan. Areas of expertise and current scientific interests Rustam Bekishev's research interests focus on statistics of extremes.

Sabrina Vettori

Ph.D. Student, Statistics
Sabrina Vettori was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the joint supervision of Prof. Marc Genton and Prof. Raphaël Huser. She successfully defended her Ph.D. thesis entitled " Models and Inference for Multivariate Spatial Extremes" and graduated in December 2017; see his PhD thesis here. Her PhD committee was composed of Professors Marc Genton (chair), Raphaël Huser (co-chair), Anthony Davison (external examiner from EPFL, Switzerland), Ying Sun and Slim Alouini. After her PhD, Sabrina co-founded the successful KAUST start-up

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Enas Alahmadi

M.S. Student, Statistics
Enas Alahmadi is a Master student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Education and Early Career Enas was selected for the KAUST Gifted Student Program (KGSP) to pursue her undergraduate studies abroad. She completed her Bachelor of Arts and Sciences in Mathematics and Bachelor of Arts and Sciences in Economics in 2018 at Boston University (MS), United States. She also completed a two month-internship at KAUST under the supervision of Prof. Raphaël Huser and Dr. Daniela Castro-Camilo.

Daniela Cisneros

Ph.D. Student, Statistics
Daniela Cisneros was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Daniela successfully defended her PhD thesis entitled " Extreme-Value Models and Graphical Methods for Spatial Wildfire Risk Assessment" on September 11th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Jorge Mateu (external examiner from Universitat Jaume I, Castillón, Spain), Hernando Ombao, and Matteo Parsani. For her next career steps, Daniela will return to Mexico where

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Bingqing Yi

Postdoctoral Research Fellow, Statistics
Bingqing Yi is a PhD graduate from University of Pau and the University of Melbourne and is working with the XSTAT group as a Postdoctoral Research Fellow supervised by Professor Raphaël Huser. Education and Early Career Bingqing Yi obtained his Bachelor of Statistics from Zhongnan University of Economics and Law in 2013, Wuhan, China, and his Master of Statistics from the Australian National University. Then, he obtained his joint PhD from the University of Pau and the University of Melbourne in 2023. Research interests Bingqing Yi's research interests focus primarily on copula theory

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> Wireless communication (26)

Signal Alignment: a Practical Way to Communicate Under Unpredictable Interference

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B1 L3 R3119

This talk introduces a practical method for reliable wireless communication amidst interference by using packet repetition and multi-antenna reception to achieve signal alignment, enabling packet recovery through geometric subspace intersection even against adversarial jammers.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

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B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Flexible, Wearable, Disposable Wireless Communication and Sensing Systems Through Additive Manufacturing - 2020

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KAUST

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems. The ability to print electronics on unconventional mediums such as plastics, papers, and textiles has opened up a plethora of new applications. In this talk, various innovative antenna and sensor designs will be shown which have been realized through additive manufacturing. A multilayer process will be presented where dielectrics are also printed in addition to the metallic parts, thus demonstrating fully printed components. Many new functional inks and their use in tunable and reconfigurable components will be shown. In the end, many system level examples of wireless sensing applications will be shown. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll and reel-to-reel printing is not far away.

Yao Song

Yao Song was a CTL lab member and graduated from KAUST. Then, he joined the University of British Columbia as a PhD student.

Yao GE

Visiting Scholar with the Department of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST)

Xiangyu Cui

I will enjoy the real fun of scientific research in KAUST. There are still many people who don’t have good access to internet.

Waqas Aman

Visiting Student (former), Electrical and Computer Engineering
Remote Internship at King Abdullah University of Science and Technology (KAUST).

Wael Al-Ghamdi

Wael Al-Ghamdi was a CTL lab member and graduated from KAUST. Then, he joined Harvard University as a PhD student.

Sofiene Barhoumi

Sofiene Barhoumi is an MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career In 2020 and after being ranked among the top 2% at the Tunisian National Exam for Entrance to Engineering Schools, Sofiene Barhoumi joined the most prestigious engineering school in Tunisia, "Ecole Polytechnique de Tunisie (EPT)" where he received multidisciplinary training. In his final year, he specialized in Signals and Systems and received his Engineering Diploma with first-class honors. Before joining

Safa Khemiri

Safa Khemiri is an MS/PhD candidate at the Communication Theory Lab (CTL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career In 2018 and after being ranked among the top 2% at the Tunisian national contest for entrance to engineering schools, Safa Khemiri joined the best engineering school in Tunisia, "Ecole Polytechnique de Tunisie (EPT)" where she received multidisciplinary training. In her final year, she specialized in signals and systems. Before joining KAUST for her Master’s degree, Safa had

Rudi Coppola

Rudi Coppola is a Master's student under the supervision of Professor Mohamed-Slim Alouini at Communication Theory Lab (CTL) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Rudi completed his Bachelor’s Degree in Electronic Engineering from the University of Pisa in 2019. He has also collaborated with 'e-team', the university’s racing team, under the electronic division. In the same year, he was admitted to KAUST as a Master's student. In 2021 he earned a Master's degree in Electrical Engineering at KAUST, presenting his thesis on radar-based road

Rose Nasser Alaslani

MS Student, Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq Al-Naffouri.

Nadhir Ben Rached

Introduction Nadhir went to RWTH Aachen University, Aachen, Germany as a ​​Postdoctoral Research Fellow​. Education and early career Nadhir obtained his M.S and Ph.D. degree in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia between 2012 - 2018. Research Interest Efficient Monte Carlo approach for rare event simulation Accurate Performance Analysis of Wireless Communication Systems Computational Finance Stochastic Differential Equations Numerical Analysis Selected Publications Graduation Project, Adaptive Monte

Ming-Cheng Tsai

Ph.D. Degree in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST).

Mazen Mohamed Aboubakrelseddik Abouelezz

Visiting student at King Abdullah University of Science and Technology (KAUST)
C

Jong-Min Kim

KAUST provides me with a great research environment and the opportunity to meet great people. Communication Engineering is a key technology that covers all engineering fields and drives my passion.

Jikang Deng

Jikang Deng is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jikang Deng obtained his bachelor's degree in Network Engineering from University of Electronic Science and Technology of China (UESTC) in 2023. He was a summer intern in 2022 before joining KAUST. Research Interest Jikang Deng is focusing in the area of wireless communication and Internet of Things (IoT). Education Profile M.S. in King Abdullah University of Science and

Jie Ma

MS/PhD student Electrical and Computer Engineering under the supervision of Prof. Tareq Al-Naffouri at King Abdullah University of Science and Technology (KAUST),

Hao Lin

KAUST provides the best environment for us to develop. At KAUST, there are many excellent Professors and friends who can study and do research together. Communication Engineering is an interesting subject that is a key tech for the future. Communication Theory is highly worthy of our in-depth research.
Ali Hanif

Ali Hanif

Ali Hanif is currently a PhD student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Alice Faisal

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST), chair of IEEE women in engineering affinity group at Effat University student branch.

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.

Abdullah Ahmed Zayat

Visiting Student Research Program under the supervision of Professor Tareq Al-Naffouri ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> Computational biology (25)

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

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KAUST

In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

A personal path from mathematics to biomedical research

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B2 L5 R5209

A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Alumni Feature: Arturo Magana Mora

1 min read ·
CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

CBRC goes to ECCB 2018

1 min read ·
The European Conference on Computational Biology (ECCB) is one of the most important conferences in the field of bioinformatics and computational biology. Held in a different European city annually, this year's conference will take place in the historic city of Athens, Greece on September 8-12, 2018.

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical

Chiheb Ben Hammouda

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include
<> computational science (25)

KAUST climate research named finalist for ADIA Lab Climate Data Sciences Award

3 min read ·
KAUST team develops breakthrough method for wind data generation, earning finalist spot for ADIA Lab’s climate science award.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Low Rank Everywhere (Almost)

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B9 L2 R2322

Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Choosing the right scheme for green communications

1 min read ·
Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Tunable sound transmission shapes up

1 min read ·
Carefully designed crystal structures can now be tuned to control how they transmit acoustic waves.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Tiny speakers break a barrier for sound

1 min read ·
A loudspeaker design could allow small devices to produce powerful low-frequency sounds.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Quieter wind beneath the wings

1 min read ·
The ability to efficiently simulate the noise generated by wings and propellers promises to accelerate the development of quieter aircraft and turbines.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Georgiy L. Stenchikov

Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.
<> spatial statistics (25)

Kriging Asymptotics

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B1 L4 R4102

Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging. Theoretical results regarding large sample convergence rates of kriging predictors remain elusive. By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.

A Distributed Implementation of the Multi-resolution Approximation for Very Large Spatial Data

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B1 L4 R4102

With data of rapidly increasing sizes in the environmental and geosciences such as satellite observations and high-resolution climate model runs, the spatial statistics community has recently focused on methods that are applicable to very large data. One such state-of-the-art method is the multi-resolution approximation (MRA), which was specifically developed with high performance computer architecture in mind.

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

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B1 L4 R4102

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

The strength of multidisciplinary research: the joint example of statistics and geoscience

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B1 L4 R4102

Different scientific branches have the potential to develop topics which would provide visibility and fame. However, comparable if not greater milestones can be achieved when researchers from totally different fields join their efforts. This seminar will summarize the scientific journey of a former member of KAUST, which spent three years here as a postdoc in statistics coming from a pure geological background, combining the best out of the two worlds. Examples of the latest researches will be provided in the context of space, time and space-time statistics, bridging it with the underlying geoscientific research questions.

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

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B5 L5 R5220

This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

1 min read ·
Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

CEMSE New Faculty: David Bolin, Associate Professor of Mathematical Statistics

1 min read ·
David Bolin is an associate professor of mathematical statistics who joined the KAUST Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division earlier this month from the Department of Mathematical Sciences at the University of Gothenburg. Before joining KAUST, Bolin received both his Ph.D. degree in mathematical statistics and M.S.c in engineering mathematics from Lund University, Sweden, in 2012 and 2007, respectively. Upon completing his Ph.D., he spent one year at Umeå University, Sweden, working as a postdoctoral fellow before moving to the Chalmers University of Technology. In 2016, Bolin became an associate professor of mathematics at the University of Gothenburg, where a year later he received the title of Docent in mathematical statistics.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Zhongwei Zhang

Ph.D. Student, Statistics
Zhongwei Zhang was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Zhongwei successfully defended his PhD thesis entitled " Flexible Extremal Dependence Models for Multivariate and Spatial Extremes" on October 25th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Jonathan Tawn (external examiner from Lancaster University, UK), David Bolin, and Ajay Jasra. For his next career steps, Zhongwei has accepted a postdoctoral position at the University of

Yan Song

Postdoctoral Research Fellow, Statistics

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Joydeep Chowdhury

Postdoctoral Research Fellow, Statistics
Joydeep Chowdhury is a postdoctoral fellow in the Spatio-Temporal Statistics & Data Science research group of Professor Marc G. Genton. His current research activities focus on problems in multivariate statistics, spatial statistics and functional data analysis.

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

David Keyes

Senior Associate to the President, King Abdullah University of Science and Technology
David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> bioscience (24)

Ali O Balubaid

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Peak performance for proteins

1 min read ·
An improved peak fitting procedure enables a better determination of protein structures.

Targeting cancer cells with tiny magnetic wires

1 min read ·
Magnetic nanowires with weak magnetic fields and low frequencies can destroy cancer cells without generating heat.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

Assessing seawater safety

1 min read ·
Elevated bacterial populations in waters near beaches and urban areas could pose a problem for desalination plants.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Driven to diffraction

1 min read ·
Optical elements that diffract light could lead to smaller and more powerful cameras.

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Computer models combat malaria

1 min read ·
A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Detecting metabolites at close range

1 min read ·
Pairing a conjugated polymer with a redox enzyme generates a fast, selective and sensitive electrochemical biosensor for metabolites.

Paper sensors remove the sting of diabetic testing

1 min read ·
Inkjet-printed device helps monitor a patient’s blood sugar levels without painful needles.

Visualization of brain structures helps to model function

1 min read ·
Computer simulations and virtual reality are used by KAUST researchers and collaborators in France to visualize the energetic coupling between neurons and astrocytes and to improve understanding of brain metabolism. The human brain uses more energy than any other organ, accounting for around 20 percent of all glucose-derived energy. Neurons can’t meet their own energy requirements: they depend on supporting glial cells and the neurovascular system to supply various forms of sugar fuel. However, it is still unclear how this neuro-glio-vasculature network manages the brain’s energy demands

Epilepsy study shows the shape of things to come

1 min read ·
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Using electronics to solve common biological problems

1 min read ·
What do an electrical engineer, an organic chemist, a materials scientist and a cell biologist all have in common? They invent and improve applications at the interface of biology and electronics.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

A pure twist on optical data transfer

1 min read ·
The finding that light can be twisted very precisely may offer fresh options for communications infrastructure.

CovMT: Tracking virus mutations across the world

1 min read ·
An interactive platform helps users visualize where SARS-CoV-2 mutations start, how wide they spread and how infectious they are.

Stefan Arold

Professor, Bioscience
Stefan Arold is a Professor of Bioscience at KAUST. He is affiliated with the Biological and Environmental Science and Engineering Division (BESE) and the Computational Bioscience Research Center (CBRC). Education and early career Prof. Arold has held faculty positions at MD Anderson Cancer Center (MDACC) in Houston, USA and Centre de Biochimie Structurale (CBS) in Montpellier, France. He received his Ph.D. degree in Molecular Biophysics from University of Montpellier I and graduated cum laude. Areas of expertise and current scientific interests Prof. Arold's research is dedicated to

Ricardo Henao

Associate Professor (former), Bioengineering
​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering ( BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division ( CEMSE), member of the Smart Health Initiative ( SHI) and the Computational Biosciences Research center ( CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering ( ECE)

Badoor Nasser

Badoor Nasser obtained PhD degree in Environmental Science and Engineering under the supervision of Professor Takashi Gojobori. Research Interest Badoor's research interests are in petroleum biotechnology ( MIC microbial influences corrosion, MEOR microbial enhance oil recovering, bio-surfactant, bio-desulfurization, etc.) Education Profile MSc., Biotechnology, AGU Arabian Gulf University, Bahrain, 2013 Bachelors, General Chemistry, University of Dammam, Saudi Arabia, 2007
<> numerical methods (24)

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Mini Course Part 2-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Mini Course Part 3-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Mini Course Part 1-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

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B1 L4 R4214

Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to

Energy and Entropy in Numerical Methods

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B9 L2 H2

Secondary quantities such as energy and entropy can be very important for numerical methods. Firstly, preserving these quantities can ensure that non-physical behavior is excluded. Secondly, preserving such quantities can result in stability estimates. Finally, preserving the correct energy/entropy evolution in time can result in additional desirable properties such as lower numerical errors. In this talk, a brief overview of some recent advances concerning energy and entropy preserving numerical methods for ordinary and partial differential equations will be given, together with an outlook on future research directions and applications.

Deterministic particle approximation for nonlinear transport equations

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Building L3 R3119

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

Mean Field Games: Numerical Methods and Applications

Mathieu Laurière, Postdoc, Department of Operations Research and Financial Engineering, Princeton University

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KAUST

In this talk, several numerical methods will be presented and illustrated on examples. Borrowing tools from stochastic analysis, optimization, partial differential equations and machine learning, these methods enable us to solve mean field games with possibly complex sources of noise or high dimensional state variables.

International Seminar on Mathematical Models and Numerical Methods for Flow and Transport in Porous Media

Speakers from KAUST, CEMSE, PSE, G-CSC, IBRAE and INM RAS

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KAUST

This workshop is devoted to numerical simulation of groundwater flow and subsurface contamination transport, as well as related problems. The main topics are mathematical modeling of the processes in porous media and the numerical methods for discretization, solution of the discretized systems and numerical treatment of inverse problems. In particular, fractured porous media and partially saturated aquifers will be concerned.

Asymptotic Analysis and Numerical Methods for Compressible Flows at Low Mach Numbers

Prof. Dr. Andreas Meister, Professor, Department of Mathematics and Natural Sciences, University of Kassel

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B1 L4 R4102

We will present a comprehensive study of a finite volume method for inviscid and viscous flow fields at high and low speeds. Thereby, the results of a formal asymptotic low Mach number analysis are used to extend the validity of the numerical method from the simulation of compressible flow fields at transonic as well as supersonic speed to the low Mach number regime.

Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

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B2 L5 R5220

In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.

Multiscale numerical methods. Theory and Practice

Yalchin Efendiev, Professor, Institute for Scientific Computation, Texas A and M University

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B9 L3 R3128

In this talk, I will talk about general approaches for multiscale modeling (closely related to porous media applications). I will mainly focus on numerical approaches, where multiscale finite element basis functions are constructed and used in approximating the solution. In these approaches, macroscopic equations are formed via some variational formulations of the problem. I will discuss how these approaches are used in deriving, so called upscaling techniques and the relation to well known upscaling methods. The concepts discussed in the talk are used for linear and nonlinear problems. I will discuss some applications.

Fast Solution Methods for Wave Propagation Problems: From Classical Domain Decomposition Solvers to Learning

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B2 L5 R5220

Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Understanding the COVID wave

1 min read ·
As countries around the world work to "flatten the curve," a reference to slowing the progression of COVID-19 cases, David Ketcheson is putting mathematical modeling to work to understand how transmission is playing out.

Kadir Akbudak

Dr. Kadir Akbudak is a Consultant at Extreme Computing Research Center at KAUST, working with Professor David E. Keyes and Dr. Hatem Ltaief. Research Interests Kadir Akbudak's research interests include Combinatorial scientific computing, Graph and hypergraph partitioning, Locality-aware partitioning and scheduling, High performance kernels for sparse computations, Vectorization for irregular applications, Iterative numerical methods and Parallel numerical algorithms. Education Profile Ph.D., Computer Engineering, Bilkent University, 2015. M.S., Computer Engineering, Bilkent University, 2009

Hakan Bagci

Professor Bagci is a highly accomplished researcher in the field of computational electromagnetics (CEM). His research focuses on the theoretical and applied aspects of CEM.

Gabriel Wittum

Dr. Gabriel Wittum, professor of applied mathematics and computational science at KAUST, is an expert in modelling and simulating problems in empirical science.

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]
<> embedded systems (23)
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Vehicle Intrusion Resilience Systems in Action

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and intrusions, which can lead to catastrophic failures that threatens human lives. A Fault and Intrusion Resilient System (FIRS) is a vehicle middleware that can mask the effect of a failure or intrusion. Contrary to

Additively Manufactured Conformal Microwave Sensors for Applications in Oil Industry

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B3 L5 R5220

Modern industries are adapting smart ways of monitoring their processes to ensure smooth operations. Sensors capable of early detection of a problem are becoming the norm in industrial processes. This is key to the development of the “Internet of Things” (IoT), in which billions of interconnected devices will work together to make smart decisions. Sensors that can detect and communicate the process information are essential ingredients of any IoT-enabled network. Since billions of such sensor nodes will be required in the future, the low cost will be an important feature for these devices. Consistent with the above-mentioned trends, the oil industry is also adapting smart monitoring and actuation mechanisms for its day-to-day operations. This thesis is focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells and also help in the safe transport of oil products from the wells to the refineries.

Muhammad Akram Karimi secures 3rd prize Best Paper Award at IMS - world's most prestigious microwaves conference

1 min read ·
Karimi's work on low cost and zero weight water-cut sensors stood out for its quality and distinctive application-direct approach. Successfully, he proved cost reductions of 100 times lower and dropping weight to 1,000 times below than the ones currently available in the market.

Ph.D. student Muhammad Akram Karimimi among Best Paper Award finalists

1 min read ·
Ph.D. student Muhammad Akram Karimimi from the research team of Atif Shamim, Assistant Professor of Electrical Engineering at KAUST and Director of IMPACT Laboratory, was one of the finalists in the Student Paper Competition at the International Microwave Symposium held in San Francisco, California, in May 2016.

CEMSE Students: Muhammad Akram Karimi

1 min read ·
Muhammad Akram Karimi describes himself as an inquisitive-minded researcher who loves to solve industrial challenges through science and technology. Karimi is a third-year Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab under the supervision of Professor Atif Shamim. His current research focus is on the design of low-cost and printed microwave sensors for various industrial applications, with a prime focus on the oil industry.

KAUST student wins global sensors and measurement systems competition

1 min read ·
KAUST master's degree student José Ilton de Oliveira Filho recently won first place at the second edition of the IEEE International Sensors and Measurement Systems Student Contest (IEEE IS&M-SC). IEEE IS&M-SC is a global competition directed at teams of advanced undergraduates, master's degree and Ph.D. students and seeks to stimulate creative ideas for sensor and measuring systems applications.

Meet KAUST new student: Ioannis Zografopoulos

1 min read ·
Ioannis Zografopoulos is a Ph.D. student who joined KAUST this fall as a Ph.D. candidate and member of the Secure Next Generation Resilient Systems (SENTRY) Lab under the supervision of Professor Charalambos Konstantinou. Zografopoulos believes that KAUST's renowned faculty, global reputation and research infrastructure make the University the ideal environment for him to develop high-quality, impactful research.

Yuxiang Liu

PhD candidate in the Networking Research Lab(NETLAB) at King Abdullah University of Science and Technology.

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Salman Sarfaraz Ghori

Education 2021.10 - Present Ph.D. Student in Electrical and Computer Engg, King Abdullah University of Science and Technology, KSA 2018.09 - 2020.10 M.S in Aerospace Engg, Sapienza University of Rome, Italy ​2009.05 - 2013.05 B.Tech in Electronics and Communication Engg, Jawaharlal Nehru Technological University HYD, India Professional Profile 2018.01 - 2018.09 Senior Engineer, ZF India Technology Center, India 2017.02 - 2018.01 Embedded Software Engineer, Global Edge, India 2014.09 - 2017.01 System Engineer, Tata Consultancy Services, India Areas of Expertise and Research Interests Space

Muhammad Akram Karimi

Muhammad Akram Karimi obtained his Ph.D. degree in Electrical Engineering under the supervision of Professor Atif Shamim at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Research Interest Muhammad's research interests included the Design of Cost-Effective and robust Fluidic Sensors to characterize fluids (esp. Crude Oil) and Energy Harvesting. Selected Publications ​Journal : M. A. Karimi, M Arsalan and A. Shamim, "Low cost and pipe conformable microwave-based water cut sensor", IEEE Sensors

Mohammed Abdullah Hussaini

Senior Embedded System Engineer at KAUST (King Abdullah University of Science and Technology)

Moetaz Abbas

Research Engineer, Computer Science
Moetaz Abbas was an Optoelectronic Engineer in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Moetaz obtained his bachelor degree in Electronics Engineering from The American University in Cairo (AUC) in Egypt in 2010. By 2011, he received his master degree in Computer Science from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. After his masters, he returned back to The American University in Cairo (AUC) as a teaching assistant for the

Jose Ilton De Oliveira Filho

Postdoctoral Research Fellow, Electrical and Computer Engineering
José Ilton is currently a Postdoctoral Fellow in the Electrical and Computer Engineering program, at King Abdullah University of Science and Technology (KAUST). José is focused on instrumentation, prototyping, and communication theory. Education and Early Career José Ilton did his bachelor’s in electrical engineering on the track of Power Systems at Federal University of Piaui with 1.5-year exchange period at Budapest University of Technology and Economics, in 2015. At King Abdullah University of Science and Technology (KAUST) he did his master’s in electrical engineering on the topic of

Ioannis (Yannis) Zografopoulos

Ioannis Zografopoulos received his Ph.D. at the Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE) of King Abdullah University of Science and Technology (KAUST) in 2023. Prior, he graduated with a B.Eng. and M.Eng. degrees in Computer, Communications, and Network Engineering, and an M.Sc. degree in Electrical and Computer Engineering from the University of Thessaly, Volos, Greece, in 2014 and 2015, respectively. His research interests include cyber-physical systems security, with an emphasis on embedded systems for industrial, distributed energy, and power grid

Hasan Albinsaid

Hasan Albinsaid is a Ph.D. student under the supervision of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Hasan received his B.Eng. degree in Electronic Engineering from Universitas Brawijaya (UB), Indonesia, in 2018 and his M.Sc. degree in Telecommunication Engineering from National Sun Yat-Sen University (NSYSU), Taiwan, in 2020. After that, he worked as a Research Assistant at the Institute of Communications Engineering, NSYSU, Taiwan. Currently, he is pursuing a Ph.D. in Electrical

Fellipe Augusto Araújo Leandro

Fellipe received his bachelor degree in Electrical Engineering from Federal University of Rio Grande do Norte (UFRN), Brazil, in 2019. During his undergraduation, he worked in researches related to microcontroller-based embedded systems platforms, with focus in embedded software programming and PCB design. In 2019, he worked as an intern in a startup company developing embedded software for a Lora-based biotelemetry system, and also in another startup developing PCB design for low-cost Educational robotics and electronics. In 2020, he joined KAUST to research under the supervision of Professor

Evangelos Piliouras

Education and early career Evangelos received his integrated master's degree with excellency, from the department of Electrical Engineering and Computer Technology in Patras in 2018. He recently joined King Abdullah University of Science and Technology (KAUST) and he works under the supervision of Prof. Meriem Laleg in the field of estimation. Evangelos did a 3-month internship in Dialog Semiconductor as a software developer, having developed a software automation tool. Research Interests Evangelos' research interests are found in the area of biosensors, embedded system design, modeling and

Christos Sapsanis

Education Profile : - Ph.D. Candidate, Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. - M.Sc., Electrical Engineering (Electrophysics), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2015. - B.Sc., Electrical and Computer Engineering, University of Patras, Greece, 2013. Introduction: Christos Sapsanis obtained his M.Sc. Degree in the Electrical Engineering (EE) Program from King Abdullah University of Science and Technology, under the supervision of Prof. Khaled Nabil Salama. Before joining KAUST, he earned his B.Sc. in Electrical

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Ahmed Eltawil

Professor Eltawil is an expert in wireless communication system design with an established history of university-industry collaboration. He continues to act as an adviser in developing wireless systems for leading international companies.

Abdullah Al-Nafisah

Master student at the Information Science Lab (ISL), in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).
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Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

Towards Collaboration of Multiple Intelligent Robots and Human Supervision

Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester

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B2 L5 R5209

As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.

Ariadne: Building safe autonomy with unproven algorithms

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KAUST

With the advent of increasingly intelligent algorithms, robots are capable of planning and performing increasingly challenging and creative tasks. Safety, however, remains an essential requirement on robotic behaviors. It is also a property that is hard or impossible to prove for virtually all intelligent algorithms of practical value. Ariadne is a model-based paradigm that enables the safe operation of many robotic systems, even though the algorithms involved with the operation may not be verifiable. Ariadne, or "plan B" engineering, will be illustrated in various current Robotics contexts derived from Ariadne's own Greek mythology, railroad systems, nuclear energy production, air transportation, and others.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

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B2 L5 R5220

Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

IXPUG Middle East Conference 2018 at KAUST

1 min read ·
KAUST will host the first Middle East meeting of the Intel eXtreme Performance Users Group (IXPUG), 22-25 April 2018. Alan Gara, Intel Fellow, will give the keynote address. Alex Heinecke (Intel), Adrian Tate (Cray), Vincent Etienne (Aramco), and Taisuke Boke (Tsukuba University) will give invited talks.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Xiaoke Yang

Visiting Scholar, Information Science Lab
PhD candidate at King Abdullah University of Science and Technology. Visiting Scholar, Information Science Lab

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).
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Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

High-dimensional Q-learning for Dynamic Treatment Regimes

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B3 L5 R5220

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

Neuro-symbolic systems in the life sciences

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B9 H1 R2322

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

1 min read ·
Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Superior seismic imaging for better drilling

1 min read ·
Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

2018 Workshop on Statistics and Data Science

1 min read ·
The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Ronell Sicat

Visualization Scientist, Core labs and Research Infrastructure
Ronell Sicat is a scientist with more than 10 years of experience in scientific research and software development. His main interests are scalable visualization and analysis of large-scale data (e.g., images, volumes, meshes), and immersive analytics that leverage augmented/virtual reality. He is passionate about helping domain scientists solve their problems using my wide-ranging experience in programming, image processing, segmentation, quantitative analysis, and computer graphics.

Olga Chechekina

Visiting Student, Bio-Ontology Research Group
Olga Chechekina is a visiting student in the Bio-Ontology Research Group under the supervision of Prof. Robert Hoehndorf. Prior to visiting KAUST, she graduated from "Biology and Biotechnology" department at HSE University in 2023, and completed an internship at Genome Institute of Singapore in 2022. Here research interests are in metagenomics, molecular biology and data analysis.

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 IT Specialist with a background in quantum engineering and data science. He leverages analytical, multidisciplinary, and data-driven approaches to drive innovation and efficiency within the IT infrastructure. He excels at full-stack development, identifying complex problems' root causes and developing impactful long-term solutions within a broad area of competence.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdullah Alharthi

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Abdullah Alharthi's research centers around pattern recognition as a means to tackle a diverse array of new challenges, including how to learn intelligent behavior in complex, dynamic environments. His areas of expertise include cognition, perception, theory of mind, gait, and human-robot interaction. His work delves into both human kinetics and the comprehension and resolution of how humans and robots interact with one another in a dynamic setting. Dr. Alharthi employs Deep Learning techniques to tackle problems related to image, object, and signal recognition and classification
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Fei Xiang

Nanomaterials and Nanostructure Based Energy Efficient Edge and Fluidic Computing for Internet of Things Applications

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B9 L2 H1

In this talk, I will address these questions with a focus on smart and multifunctional nanomaterial-based memory devices than can sense different physical qualities of the environment (Memsors) in addition to pressure-driven microfluidic logic gates which can process and analyze non-electronic media in an attempt to explore, enable and empower a wider range of IoT applications.

Nanomaterials and Nanostructure Based Energy Efficient In-Memory Sensing

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KAUST

In this talk, I will present the potential of nano-islands grown by the atomic layer deposition (ALD) in addition to novel material combinations in scaled memory devices. Moreover, in order to relax the scalability requirement, I will introduce an integrated potential single device solution by infusing memory device which can monitor and remember our actions and the surrounding environment. Such a device performs the roles of both a MEMory and a senSOR at once and is referred to as a MEMSOR. The MEMSOR is directly programmed by external physical stimuli rather than an applied electric potential as in conventional devices. As a result, the MEMSOR leads to faster data analysis, lower footprint area, energy consumption, and ideally, the cost of the system. Finally, the MEMSOR device will be based on a MOSFET architecture with a mature manufacturing process, and thus it can be integrated with conventional Flash devices on the same silicon wafer with minimal additional process steps.

Applications of Magnetic Transition Metal Dichalcogenide Monolayers to the Field of Spinorbitronics

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KAUST

Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.

CEMSE Welcomes new Professor: Dr. Jr-Hau

1 min read ·
Prof. He joins from National Taiwan University, where he was a tenured associate professor of Electrical Engineering.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

Visiting Ph.D. Student Meng-Lin Tsai and Prof Jr-Hau He device the thinnest highly-efficient solar cell

1 min read ·
At King Abdullah University of Science and Technology (KAUST), a team of researchers developed what might be the effective solution to the problem: the thinnest solar cells with lateral geometry offering high optoelectronic performances while reducing production costs of extended photovoltaic architecture. The research was recently published in Advanced Material and reported by Nature Middle East.

PhD candidate Amal Mohammed Alamri gets a research grant from GPURC

1 min read ·
KAUST Electrical Engineering PhD candidate Amal Mohammed Alamri, was recently awarded a research grant by the Grants Programs for Universities and Research Centers (GPURC) . The Graduate Research Program is a new long-term strategic program in the Saudi research and development support ecosystem aimed at boosting Saudi Arabia's position among leading countries in the field of science, technology and innovation. The GPURC is one of the NTP (2020) initiatives supervised by the King Abdulaziz City for Science and Technology (KACST) which is the Saudi government's national laboratory and funding agency for scientific programs across the kingdom.

Jr-Hau He elected SPIE Fellow

1 min read ·
Jr-Hau He, KAUST associate professor of electrical engineering, has recently been elected as an SPIE Fellow for his achievements in solar cells, photodetectors and semiconductor optics research. SPIE, the international society for optics and photonics, annually recognizes society members who have made significant scientific and technical contributions in the multidisciplinary fields of optics, photonics and imaging, and for their outstanding technical contributions and service to SPIE. He and 72 additional fellows of the society will be promoted later this year.

Photoelectrochemical research wins gold and silver in Hong Kong

1 min read ·
The 2018 NANO Conference, which took place at the City University of Hong Kong in late June, was spearheaded by the International Committee on Nanostructured Materials (ICNM) and is a traditional meeting place for nanomaterials researchers. There, state-of-the-art research and the latest advanced findings in the field are presented. Ph.D. student Hui-Chun Fu and postdoctoral fellow Purushothaman Varadhan, researchers from KAUST Professor Jr-Hau He's lab, presented the findings of their research at the conference, winning two prestigious awards: the Materials Today Rising Star Poster Award

KAUST Associate Professor Jr-Hau He wins Nano Energy Award 2019

1 min read ·
The Beijing Institute of Nanoenergy and Nanosystems of the Chinese Academy of Sciences recently announced that KAUST Associate Professor Jr-Hau He is one of three selected scholars who will receive the Nano Energy Awards 2019.

Baby steps for transparent electronics

1 min read ·
Stretchy, see-through, silver nanowire sheet combines optical transparency with excellent electrical conductivity.

Veerappan Mani

Research Scientist, Sensors
Dr. Veerappan Mani is currently a Research Scientist at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). He is one of the world’s top 2% scientists since 2020. Education and Early Career Dr. Veerappan Mani received his Ph.D. from the Department of Chemical Engineering and Biotechnology, National Taipei University of Technology (NTUT), Taiwan in 2014. He worked as a research assistant professor at the Institute of Biochemical and Biomedical Engineering, Taipei Tech, Taiwan, from 2016 to 2019. He was a research visiting

Mohamed Ebaid

Postdoctoral Research Fellow, Photonics Laboratory
Currently works at ​Lawrence Berkeley National Lab, California, USA

Khalid El Abd

Khalid El Abd is currently a M.S. student Graduand at Sapienza University, Rome, Italy. He has been selected for the Visiting Students Research Program offered by KAUST. During his Internship, he is conducting his research at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Khalid El Abd obtained the B.S. from the Faculty of Civil and Industrial Engineering, Major in Clinical Engineering, Sapienza University of Rome, Italy. After starting his M.S., in Biomedical Engineering at Sapienza

Jung-Wook Min

Research Scientist, Photonics Laboratory
Introduction Jung-Wook Min is a Research Scientist at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Science and Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). He received his M.S. and a Ph.D. degree in the Department of Physics and Photon Science at Gwangju Institute of Science and Technology (GIST), Republic of Korea, in 2012 and 2016. He has 10 years of hands-on experiences of refurbishment, modification and maintenance of various type of III-N and III-As/P MBE chambers and has been focused on the epitaxial growth of III

Idris H. Smaili

Idris H. Smaili is a Ph.D. student in the electrical and computer engineering program at Computational Physics & Materials Science Lab under the supervision of Prof. Udo Schwingenschlogl at King Abdullah University of Science and Technology (KAUST). Idris obtained his bachelor's degree in Electrical Engineering, Electronics, and Communications from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia, in 2007. After that, he joined Rochester Institute of Technology (RIT), Rochester, NY, USA, to receive his master's degree in Microelectronic Engineering in 2014. Idris is a lecturer at the

Husam N. Alshareef

Professor Husam Alshareef leads research in nanomaterials for energy storage and electronics.

Huafan Zhang

Huafan Zhang is currently a Ph.D. Student in the Photonics Laboratory. She obtained her BEng. (2017) in Electronic science and technology from the University of Electronic Science and Technology of China (UESTC), Chengdu, China. She joined King Abdullah University of Science and Technology (KAUST) in the Fall of 2017 and achieved MSc in Electrical Engineering at KAUST in 2018. Her current research focuses on III-Nitride nanowire-based photoelectrochemical water splitting, radiative cooling for optoelectronic devices and materials for water collections. Research Interests Her research interests

Chandu V.V. Muralee Gopi

Postdoctoral Research Fellow, Photonics Laboratory
Chandu V.V. Muralee Gopi is a Postdoctoral Fellow at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Science and Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). He received his Master's degree in Interdisciplinary Program in Robotics and a Ph.D. degree in Electrical Engineering from Pusan National University, South Korea, in Feb. 2015 and Feb. 2018 respectively. Over the past 6 years, he has held multiple appointments, within South Korea, and has developed broad expertise and a proven track record in the development of highly

Amal M. AlAmri

Amal Al-Amri is a Ph.D. candidate in Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jr-Hau-He. Amal received her B.Sc. in Physics from King Abdulaziz University in Saudi Arabia and proceeded to obtain her M.Sc. with a concentration in Nanomaterial's Science in Physics from King Abdulaziz University. As an active member of the Saudi Association for Physical Sciences and a member of the Talented Center King Abdulaziz University, she was able to win the Innovative Award at King Abdulaziz University in 2008 and got the second

Ainur Sharip

Ph.D. Student, Bioscience
Ainur Sharip obtained her master's degree under the supervision of Associate Professor Jürgen Kosel in the University's Computer, Electrical and Mathematical Science & Engineering division. As a member of Kosel's Sensing, Magnetism and Microsystems (SMM) research group, Sharip and her colleagues investigated the behavior of stem cells upon culturing on magnetic iron nanowire substrate, with a specific focus on cytoskeleton rearrangement and the differentiation of cells. Currently a Ph.D. student in Bioscience, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
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ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Uncertainty Quantification of Tsunami Models

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B1

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

Decision-Making Under Risk in Complex Environments

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B9 L2 H2

Advances in uncertainty quantification enable more nuanced exploration of decision-making under risk in complex environments.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Cross-disciplinary collaboration results in groundbreaking earthquake research

1 min read ·
For KAUST research assistant Dr. Ling Zhang and Ph.D. student Hoby Razafindrakoto, a class in statistics the two took together on campus was a serendipitous event. "Hoby and Ling came to my spatial statistics class in the spring of 2013," explained Marc Genton, Professor of Statistics in the Computer, Electrical and Mathematical Science and Engineering Division. "They had to do a project for the class, and their project morphed into a collaboration that produced two papers and an online tool for uncertainty quantification in earthquake source modeling." The group effort included P. Martin Mai

Study finds Red Sea may be cooling rather than warming

1 min read ·
A recent study by a team including KAUST Earth scientists and oceanographers revealed that surface temperatures in the Red Sea may be cooling rather than rising. Analysis of long-term data sets shows that the current high warming rates of the Red Sea appear to be a combined effect of global warming and natural long-duration changes in sea surface temperature. "Our study has revealed a sequence of alternating positive and negative trends in Red Sea surface temperatures. Over the next decades, the trends indicate a cooling phase that may counter the effects of global warming," stated Ibrahim

Red Sea temperatures to cool in coming decades

1 min read ·
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Sören Wolfers

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Saja Almohammadi

Saja Almohammadi is a PhD candidate in Applied Mathematics and Computational Sciences. She is studying under supervision of Professor Omar Knio. Research Interest Saja Almohammadi's research interest include working on Uncertainty Quantification (UQ) on chemical system. Development of methods for forward UQ as applied to chemical kinetic application relevant to Rapid compression machine on iso-octane. Education Profile M.Sc. in Applied Math, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2014. BS Mathematics Department, King Abdelaziz University (KAU), Saudi Arabia

Ricardo Manuel Pinto de Lima

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Quan Long

Quan Long worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Quan obtained his Ph.D. degree in computational structural mechanics from CSMLab at Cambridge University, where his research focused on isogeometric analysis using subdivision techniques and its applications in large structural deformations, nematic elastomers, and dynamic fractures. During his postdoctoral research, he extended his interests from deterministic problems to stochastic problems, for example, Bayesian

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Marco Ballesio

Marco Ballesio is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Probability Theory and Stochastic Processes, Stochastic Modeling, Uncertainty Quantification. Education Profile Laurea Magistrale (Master’s Degree) in Mathematics for Engineering, Politecnico di Torino (Grade: 110/110 cum laude), (2013-3/2016). Allievi Honors Program, Allievo Senior, Collegio Carlo Alberto (2013-3/2016). Erasmus Student at Aalto

Ibrahim Hoteit

Professor Ibrahim Hoteit is a faculty member at KAUST, specializing in the simulation, analysis, and prediction of weather and climate patterns across the Arabian Peninsula. His pioneering research leverages data-driven modeling systems to advance the understanding of climatic dynamics in the Red Sea and Arabian Gulf regions. Professor Hoteit's work not only contributes to regional climate science but also supports sustainable environmental management and policy development in these critical areas.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]
<> Bayesian computational statistics (21)

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Exploring Non-Linear Interactions in Multivariate Time Series

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KAUST

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

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B3 L5 R5209

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

Bayesian Deep Learning

Maurizio Filippone, Associate Professor, EURECOM, France

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B9 L3 R3128

The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

Approximate Bayesian inference based on dense matrices and new features using INLA

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B4 L5 R5220

The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

KAUST Ph.D. candidate wins ISBA-BayesComp poster award at the International Society for Bayesian Analysis 2022

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Esmail Abdul Fattah, a KAUST Ph.D. candidate in statistics, won a poster award in the "junior-BayesComp-ISBA" category at the 30th meeting of the International Society for Bayesian Analysis (ISBA 2022) held from June 26 to July 1 in Montreal, Canada.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Miguel Angel Alvarez Ballesteros

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela

Małgorzata Forystek

M.S. Student, Statistics
Statistics Master's student interested in Data Science

Janet van Niekerk

Research Scientist, Statistics
Janet van Niekerk is a Research Scientist at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Mathematical Statistics), University of Pretoria, 2014 PhD (Mathematical Statistics), University of Pretoria, 2017 Research interests Efficient Bayesian methods for practical implementation, statistics for medical applications, complex survival analysis, INLA. Awards World Bank Fund awardee, International Statistical Institute, 2013 and 2015 Best oral presentation, Statistical

Hanan Alahmadi

Ph.D. Student, Statistics
Hanan Alahmadi is a PhD candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Education Profile M.Sc. Statistics, KAUST, 2021 B.Sc. Mathematics, Taibah University , 2017 Research Interest Geospatial data analysis and health surveillance Spatio-temporal disease data Statistics and spatial epidemiology Geospatial modeling Disease mapping Location Building 1, Al-Khawarizmi, 4th floor, 4212-WS12 Publication Joint Quantile Disease Mapping

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Djidenou Montcho

Ph.D. Student, Statistics
Djidenou (Hans) Montcho is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Hans obtained his Bachelor Degree in Applied Mathematics at the Federal University of Rio Grande in Brazil. He graduated with a Master of Science from the Federal University of Sao Carlos and the University of Sao Paulo. He has worked as a data scientist for a large credit bureau in Brazil. Research Interest His research interest is mainly in Bayesian and

Denis Rustand

Postdoctoral Research Fellow, Statistics
Denis Rustand is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Statistics), University of Southern Brittany, 2017 PhD (Biostatistics), University of Bordeaux, 2020 Research interests Bayesian computational statistics, survival analysis, applications of statistics to medical research, INLA. Awards Doctor Norbert Marx Award, 2021 Summer school grant, Univerity of Toronto, 2018 Académie Française, Jean Walter Zellidja grant, 2018 EHESP

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Abylay Zhumekenov

Ph.D. Student, Statistics
Abylay Zhumekenov is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Abylay obtained his Bachelor of Science in Mathematics in 2017 from Nazarbayev University, Astana, Kazakhstan. He then obtained a Master of Science degree in Applied Mathematics from Nazarbayev University in 2019. Research Interests Abylay's main research interests are Bayesian computational statistics and numerical methods for large sparse systems.
<> marine science (21)

Unlocking the mysteries of Red Sea eddies

1 min read ·
First-time reports of the statistical properties of ‘whirlpools’ show they are frequent and seasonal.

Coral reef fish: not such a family affair

1 min read ·
Some coral reef fish species don’t settle very close to home.

Assessing seawater safety

1 min read ·
Elevated bacterial populations in waters near beaches and urban areas could pose a problem for desalination plants.

The Red Sea models the future

1 min read ·
Detailed analysis of nutrient distribution and circulation in the Red Sea could provide a model for the future of the world’s oceans.

Smart technology shows where we're at

1 min read ·
A combination of state-of-the-art sensors and standardized data analysis will transform research for tracking animals and humans.

An ocean observatory for the Red Sea

1 min read ·
Studies conducted at the Saudi Aramco-KAUST Marine Environmental Research Center provide new insights into the physical and biological aspects of the Red Sea.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Whale shark populations are young and transient

1 min read ·
A population study of whale sharks in the Red Sea reveals unique group dynamics.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Deep thinking brings underwater robot to life

1 min read ·
An international, multidisciplinary collaboration that led to the world’s first underwater robotic avatar.

No strings attached for underwater video system

1 min read ·
An underwater wireless optical communications system for streaming high-quality, live video.

Microbial communities have a seasonal shake-up

1 min read ·
Turbulence and nutrient availability drive changes in Red Sea microbes.

Smart skin for flexible monitoring

1 min read ·
An electronic tag that stretches and flexes while it records location and environmental data can monitor marine animals in their natural habitat.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Mysterious gentle giants gather off Saudi coast

1 min read ·
Investigations of prey patterns fail to explain why whale sharks aggregate off the coast of Saudi Arabia.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Mining Red Sea bacteria for industrial potential

1 min read ·
Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Marine sensor gets to grips with salt

1 min read ·
Laser-induced graphene formation creates tailor-made sensors for monitoring ocean creatures and ecosystems.

Marine Skin dives deeper for better monitoring

1 min read ·
A pioneering tagging system that monitors the movement and local environment of sea animals reaches deeper depths and higher sensitivities.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.
<> nanoelectronics (21)
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

A voice to the voiceless: Montserrat Ramirez De Angel takes “Best Pitch” award at IEEE 2024 Sensors Conference

3 min read ·
Montserrat Ramirez de Angel wins the Best Pitch Award at IEEE 2024 Sensors Conference for her innovative work in biosensors and sensor technology.

KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

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KAUST

The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together.

Prof. Muhammad Hussain selected as distinguished lecturer for the IEEE Electron Devices Society

1 min read ·
Prof. Muhammad Hussain has been selected as a distinguished lecturer for the IEEE Electron Devices Society (EDS). The IEEE sponsored EDS Distinguished Lecturer Program exists for the purpose of providing IEEE EDS Chapters with a list of quality lecturers.

Prof. Hussain named Fellow of Institute of Nanotechnology, UK

1 min read ·
Dr. Muhammad Mustafa Hussain an Assistant Professor of Electrical Engineering from founding batch faculty has been named as the Fellow of Institute of Nanotechnology, UK.

KAUST PhD: UC Berkeley Best Research Award 2014

1 min read ·
KAUST CEMSE/EE graduate Dr. Hossain Fahad has just received the Best Research Award among 150 UC Berkeley research scientists, post-docs and graduate students.

KAUST team achieves remarkable flexibility with Silicon

1 min read ·
KAUST's Integrated Nanotechnology Lab are working to transform cutting-edge materials to make high-performance devices, but also about portability - they will have to be flexible, stretchable and reconfigurable

Stretchable Electronics Make Thermotherapy Easier for Arthritis Patients

1 min read ·
A paper has been published to cover a new technology that makes electronic devices stretchable while retaining their electrical and thermal properties.

Scientists made cutting edge 'smart skin' with household items

1 min read ·
Congratulations to Prof. Muhammad Hussain whose smart skin project made out of cheap household materials such as foil and tape - was featured on the Washington Post and a few other news media.

The Institute of Physics (IoP), nominated Prof. Muhammad Mustafa Hussain for the Fellowship at the Institute of Physics

1 min read ·
Prof. Muhammad Mustafa Hussain nominated for the Fellowship at the Institute of Physics (IoP), UK.

Prof. Muhammad Hussain's article selected for "2015 Applied Physics Letters Editors' Picks

1 min read ·
Prof. Muhammad Hussain's article "Functional integrity of flexible n-channel metal-oxide-semiconductor field-effect transistors on a reversibly bi-stable platform" was selected for "2015 Applied Physics Letters Editors' Picks"- published by the American Institute of Physics - a scientific journal featuring concise, up-to-date reports on significant new findings in applied physics.

Muhammad Hussain elected Fellow of the American Physical Society (APS)

1 min read ·
The Fellowship is an international distinction assigned only to highly selected scientists for their contributions in the fields of APS, and was given to Prof Hussain for contributions to exploration, evaluation, and transition of planar and nonplanar high-k/metal gate complementary metal oxide semiconductor electronics, silicon/silicongermanium/ III-V nanotube devices, and flexible, stretchable, reconfigurable complementary metal–oxide–semiconductor electronic systems.

Self-destructible electronics is a Mission Impossible no more

1 min read ·
In today's digital age, the increasing dependency on information makes everyone potentially exposed to privacy invasion and cyber security. Annual reports on tech-safety breaches are alarming, with stolen portable devices, laptops or mobiles, along with hacked hardware skyrocketing to 50% in the United States only. Bigger the picture, global is the risk of unwilling access to private lives or strategical documents, with everyone in the eye of the most dangerous of information violation. In such cases, an immediate ability to remotely destroy the sensitive parts, if it is not possible to regain them, would save us.

Wrist sensor for health screening

1 min read ·
Give a Post-It Note paper to an Electrical Engineering team and you will see it turn onto the new alley to revolutionize modern medicine; an unprecedented wearable, budget-friendly and reliable a paper-based sensor for health screening encapsulated in a silicon wrist with a recycling high-tech design. Body temperature, sweat levels, heart rate and blood pressure can now be monitored with no need to go to hospitals nor using costly technologies, dramatically rare in underdeveloped or developing countries.

Dental braces get smart

1 min read ·
-By Francesca Serra Muhammad Hussain, associate Professor of electrical engineering at the Computer, Electrical and Mathematical Science and Engineering Division of KAUST, recently developed a new dental brace technology that can straighten teeth faster and more effectively than actual products. The orthodontic system was designed to induce faster bone regeneration and to enhance protection of the enamel. According to Hussain and his team, the technology is going to introduce a paradigm shift in orthodontic treatments "The integration of high performance inorganic mechanically conformable and

UESTC students visit CEMSE Laboratories during a one week Winter Camp at KAUST

1 min read ·
Twenty-two undergraduate students from the University of Electronic Science and Technology of China (UESTC), Chengdu, are currently visiting the CEMSE Division as part of one week Winter Camp at KAUST.

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

Democratizing electronics one step at a time

1 min read ·
"You would not believe how many amazingly talented people there are in the world, but they often are just not exposed to opportunities," noted Muhammad Mustafa Hussain, KAUST professor of electrical engineering and currently a visiting professor at the University of California, Berkeley. He is determined to try and change this—one step at a time.

KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

1 min read ·
The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” was held in KAUST between the 25th and 27th of February. This yearly event aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together, including Chemistry, Electrical Engineering, Biology, and Material Science, who are at the top of their

Professor Hussain elected IEEE Fellow

1 min read ·
Muhammad Mustafa Hussain, KAUST professor of electrical engineering, was recently elected as an Institute of Electrical and Electronics Engineers (IEEE) Fellow, effective January 1, 2020, for his “contributions to flexible and stretchable electronic circuits.

Nazek El-Atab chosen to attend the 70th Lindau Nobel Laureate Meeting

1 min read ·
Nazek El-Atab, a postdoctoral research fellow, based in the KAUST MMH Labs has been chosen to attend the 70th Lindau Nobel Laureate Meeting. After completing a thorough multi-stage application and selection process, El-Atab was selected to participate in the event scheduled to take place from June 28 to July 3, in Lindau, Germany.

Hossain Fahad

Hossain Fahad received both, his Master's and Ph.D. Degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Muhammad Mustafa Hussain. He is currently a post-doctoral researcher leading sensors research Prof. Ali Javey`s lab at EECS, UC Berkeley. He has authored more than 20 journal publications and his expertise is in the advanced device and sensor technology. Fahad is proficient in epitaxy, chemical vapor deposition, physical vapor deposition (sputtering and evaporation), atomic layer deposition, lithography, reactive ion
<> genomics (20)

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Digital Health: promises and pitfalls

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B9 L3 R3223

This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Mining Red Sea bacteria for industrial potential

1 min read ·
Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Hajime Ohyanagi

Research Interests ​My original question in biology is the Origin and History of Eukaryotes. I also love to understand the possibility of Genomic Breeding in the imminent global food security issue. To these ends, currently, I aim to determine the biological significance in big data of marine and crop genomics, by the means of diverse bioinformatics methodologies. Professional Profile 2015-present: Technical Specialist, KAUST, Thuwal, Saudi Arabia 2015-present: Visiting Research Scientist, National Agriculture and Food Research Organization, Tsukuba, Japan 2013-present: Visiting Research
<> human health (20)

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Computer models combat malaria

1 min read ·
A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Algorithm turns cancer gene discovery on its head

1 min read ·
A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

Sensing a heart attack before it strikes

1 min read ·
Nanomaterial-based electronic device monitors a key heart health biomarker.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

CovMT: Tracking virus mutations across the world

1 min read ·
An interactive platform helps users visualize where SARS-CoV-2 mutations start, how wide they spread and how infectious they are.

Coronavirus testing made quick and easy

1 min read ·
New diagnostic technique could help contain the spread of COVID-19 and other viral outbreaks.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Airborne antennas to address electro-magnetic fields exposure concerns

1 min read ·
Movable airborne antennas receiving cellphone signals could reduce EMF exposure while offering higher data transmission speed and using less power.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Getting to the bottom of deadly weather

1 min read ·
Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Bone formation comes down to the nanowire

1 min read ·
Jasmeen Merzaban, Jürgen Kosel and colleagues from their labs developed nanotechnology that accelerates the transition of stem cells into bone and could advance regenerative medicine.
<> mean-field games (19)

Part 3: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems.

Part 1: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 1: I will show how to transform suitable mean-field game (MFG) systems into infinite-dimensional convex optimization problems. Furthermore, I will present Uzawa’s algorithm and augmented Lagrangian approach for solving convex optimization problems. Finally, I will demonstrate how to apply these methods to approximate solutions of corresponding MFG systems.

Part 2: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 2: I will present new Fourier approximation techniques for nonlocal MFG systems and discuss how these techniques fit the variational framework presented on Day 1. Then I will introduce the Chambolle-Pock primal-dual hybrid gradient optimization method and apply a variant of this method to approximate nonlocal MFG models via Fourier approximation techniques above.

Stationary Mean-Field Games with Congestion

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B2 L5 R5220

Mean-field games MFG are models of large populations of rational agents who seek to optimize an objective function that takes into account their state variables and the distribution of the state variable of the remaining agents. MFG with congestion model problems where the agents’ motion is hampered in high-density regions. First, we study radial solutions for first- and second-order stationary MFG with congestion on R^d. Next, we consider second-order stationary MFG with congestion and prove the existence of stationary solutions. Additionally, we study first-order stationary MFG with congestion with quadratic or power-like Hamiltonians.

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

Two-Scale Homogenization and Numerical Methods for Stationary Mean-Field Games

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KAUST

Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statistical information on the population's distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove the existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltonians and Mather measures, which arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, and Aubry--Mather theory. Thus, we develop algorithms to solve the homogenized problem, effective Hamiltonians, and Mather measures.

Mean Field Games: Numerical Methods and Applications

Mathieu Laurière, Postdoc, Department of Operations Research and Financial Engineering, Princeton University

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KAUST

In this talk, several numerical methods will be presented and illustrated on examples. Borrowing tools from stochastic analysis, optimization, partial differential equations and machine learning, these methods enable us to solve mean field games with possibly complex sources of noise or high dimensional state variables.

Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

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B9 L2 R2322

Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.

Numerical approximations of Mean-Field-Games

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B1 L2 R2202

In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.

Mean Field Games price formation models

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B1 L4 R4214

This thesis studies mean-field games (MFGs) models of price formation. The thesis focuses explicitly on a MFGs price formation model proposed by Gomes and Saúde. The thesis is divided into two parts. The first part examines the deterministic supply case, while the second part extends the model to incorporate a stochastic supply function. We explore different approaches, such as Aubry-Mather theory, to study the properties of the MFGs price formation model and alternative formulations using a convex variational problem with constraints. We propose machine-learning-based numerical methods to approximate the solution of the MFGs price formation model in the deterministic and stochastic setting.

Hessian-dependent Functionals: Mean Field Games, Free Boundaries and Improved Regularity

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B2 L5 R5220

Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.

KAUST Ph.D. students win best paper awards at mean-field games conferences

1 min read ·
By Sonia Turosienski KAUST Ph.D. students David Evangelista and Xianjin Yang, who are supervised by Diogo Gomes, professor of applied mathematics and computational science, won best paper awards at conferences this summer for their work in mean-field game (MFG) theory. Mean-field games model and seek to explain the behavior of rational agents in a competitive environment and have been used in diverse areas of research, including studies on non-renewable resources and mining models; opinion dynamics; price impact and order book modeling; and networks and energy management. Evangelista was

Peter Markowich elected Fellow of the European Academy of Sciences

1 min read ·
KAUST Professor Peter Markowich has been elected as a Fellow of the European Academy of Sciences (EurASc). Established in 2003, the EurASC is a fully independent international organization of distinguished scholars that aims to recognize and elect to its membership the best European researchers to strengthen European science and scientific cooperation.

Professor Markowich elected to the European Academy of Sciences and Arts

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.

Xianjin Yang

Xianjin Yang's research interests fall on mean-field games, which is an active research area in differential game theories. In his PhD period, Xianjin focuses on the homogenization and numerical methods for stationary mean-field games. Education and Early Career Xianjin Yang got his bachelor's degree in Software Engineering from Chongqing University of China in 2011. Then, he obtained his Master with majors in Computer Science from Zhejiang University of China in 2014. Later on, Xianjin Yang received his second Master's degree in Applied Mathematics from KAUST in 2016. He continues to study at

Peter Markowich

Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Sciences
A distinguished professor of applied mathematics and computational science, Dr. Markowich is the author of over 200 publications in leading international journals and circa 14,000 citations.

Julian David Gutierrez Pineda

Julian received both B.S. in Mathematics and B.S. in Engineering from Escuela Colombiana de Ingenieria, Colombia. After receiving his M.Sc. in Mathematical Sciences from Universidad Nacional Autónoma de México, México, he joined Professor Diogo Gomes as a Ph.D. student in applied mathematics at King Abdullah University of Science and Technology, Saudi Arabia. His research interests are primarily optimal control, optimal transport, calculus of variations, partial differential equations, and mean-field games.

Diogo Gomes

Professor Diogo Gomes, a distinguished expert in Applied Mathematics and Computational Science at KAUST, leverages advanced mathematics – including partial differential equations, numerical methods and mean-field game models – to solve complex problems in social sciences, economics and finance

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> optics (19)

Professor Wolfgang Heidrich elected as a Fellow of the National Academy of Inventors and named a 2025 Optica Fellow

5 min read ·
KAUST Professor Wolfgang Heidrich has been named a Fellow of both the National Academy of Inventors (NAI) and Optica, recognizing his pioneering contributions to computational imaging, display systems and AI-driven optical technologies.

Prof. Hakan Bagci Elevated to Senior Member of IEEE

1 min read ·
Prof. Hakan Bagci has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

Humboldt Research Award for Prof. Heidrich

1 min read ·
The Alexander von Humboldt Foundation has awarded a Humboldt Research Award to Prof. Wolfgang Heidrich.

Optical chips harness the power of rogue waves

1 min read ·
A microchip designed to generate and control rogue waves of light on the nano-scale has many potential applications.An optical chip to create and harness the power of nanoscale rogue waves of light could ultimately lead to better prediction systems for weather events and natural disasters by improving researchers’ understanding of the mechanisms that underpin chaotic wave events. Natural catastrophic events are inherently unpredictable, stemming from high-energy, chaotic systems that are difficult to understand or emulate. Andrea Fratalocchi, and his team from KAUST, in collaboration with

The Orange Laser

1 min read ·
In the last issue of Electronics Letters of 9th July 2015 Vol.51 No.14 a team of researchers coordinated by Prof. Boon Ooi, director the Photonics Laboratory at King Abdullah University of Science & Technology (KAUST), presented a phosphide-based laser structure that can operate in the orange wavelengths at room temperature.

Flat lenses with a twist!

1 min read ·
Overlaying two film layers patterned with a nanoscale array can manipulate the propagation of light to create a powerful ultrathin lens.

Professor Bagci Elevated to Senior Membership Status of the International Union of Radio Science

1 min read ·
Hakan Bagci, KAUST Associate Professor of electrical engineering, was recently elevated to Senior Membership of the International Union of Radio Science (URSI) for his research achievements in the field of computational electromagnetics, and his long-standing involvement in the URSI, which includes serving on several conference technical program committees and editorial boards of journals.

Chun Hong Kang receives SPIE Optics and Photonics Education Scholarship

1 min read ·
Chun Hong Kang, a Ph.D. student in the KAUST Photonics Laboratory, has been awarded a 2021 Optics and Photonics Education Scholarship by SPIE for his “potential contributions to the field of optics and photonics.”

Outstanding KAUST research acknowledged by leading photonics journal

1 min read ·
KAUST professors Boon Ooi and Osman Bakr and their respective research groups have recently had their collaborative research recognized by one of the major journals for optics and photonics, Light: Science & Applications (LSA). The teams’ paper— titled "High-speed color-converting photodetector with all-inorganic CsPbBr3 perovskite nanocrystals for ultraviolet light communication—" was awarded as an “Outstanding Paper of Light: Science & Applications in 2020.”

Professor Ooi appointed Editor-in-Chief of IEEE Photonics Technology Letters

1 min read ·
KAUST Professor of Electrical and Computer Engineering Boon S. Ooi has been appointed Editor-in-Chief (EiC) of the IEEE Photonics Technology Letters beginning January 1, 2022.

Wolfgang Heidrich

Professor, Computer Science
Professor Heidrich is apinoeer in the development of novel sensing and display technologies by combining computer graphics, machine vision, imaging, inverse methods, optics and perception.

Qilin Sun

Qilin Sun is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qilin Sun obtained his bachelor degree in Optical Information Science and Technology from Huazhong University of Science and Technology in Wuhan, China in 2015. After that, he joined KAUST and received his master degree in Electrical Engineering in 2017.Qilin is a CEO&Founder at Point Spread Technology, China. Research Interest Qilin is interested in Computational

Mohammed Abdul Majid

Postdoctoral Research Fellow, Photonics Laboratory
Mohammed Abdul Majid was a Postdoctoral Fellow at Photonics Laboratory at King Abdullah University of Science and Technology (KAUST), where he worked on applying III/V semiconductor quantum dot/quantum well devices to applications such as biomedical imaging, optical communications to solid-state lighting. Mohammed received his M.Sc. degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia in 2002. In 2011, he obtained his Ph.D. degree in Electronic and Electrical Engineering from the University of Sheffield in the U.K. From 2002-2008

Mohamed D. Farhat

Research Scientist, Waves in Complex Media
Mohamed Farhat is currently a Research Scientist at King Abdullah University of Science and technology (KAUST), Thuwal, Saudi Arabia.

Hakan Bagci

Professor Bagci is a highly accomplished researcher in the field of computational electromagnetics (CEM). His research focuses on the theoretical and applied aspects of CEM.

Hadi Amata

Hadi Amata is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Amata received his master degree in Electronics/Microelectronics from Provence University in France in 2008. He obtained a doctoral in Electrical Engineering “Optics and Photonics” from Jean Monnet University of Saint Etienne in France in 2012. He started his career life as a teaching assistant at Saint-Étienne Institute of Technology (IUT) in 2010. He was also a teaching

Damián San Román Alerigi

Damian San Roman obtained his Master's and a Ph.D. degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). He joined KAUST in 2010 as a founding class member within the Master’s program in Photonics. He studied dynamic optical transformations for Ph.D. Damian’s Ph.D. work focused on the understanding and modeling of how different physical phenomena affect the permittivity and permeability tensors of materials and material-structures. How such effects (Aharonov-Bohm, Kerr, Thermal Cassimir, plasmonics, and other

Boon Ooi

Professor Ooi is a pioneer in optoelectronics and photonic integrated circuits development, particularly for optical communications and sensor applications. His work has led to the successful commercialization of semiconductor lasers, broadband light sources, and optical sensors

Aigars Ekers

Short bio Dr. Ekers obtained his MS and PhD in physics from the University of Latvia in 1994 and 1999, respectively. During the PhD studies, he spent periods of time performing experiments at Lund University (Sweden), Warsaw University (Poland), and Kaiserslautern University (Germany), the latter as a DAAD research scholarship holder. After a successful proposal to EU Framework Programme, he returned to Kaiserslautern University as a Marie Curie Fellowship holder (1999-2002). After the Fellowship, he continued in Kaiserslautern as a research associate. In 2005, he moved to Riga to assume a
<> algorithm (18)

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.

Unveiling the potential of energy/entropy stable numerical methods for hyperbolic/mixed hyperbolic-parabolic PDEs

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B9 L2 H1

Abstract The demand for increasingly multidisciplinary reliable simulations, for both analysis and design optimization purposes, requires transformational advances in individual components of future PDEs solvers. At the algorithmic level, hardware compatibility and efficiency are of paramount importance in determining viability on future hardware. However, equally important (if not more so) is provable algorithmic robustness which becomes progressively more challenging to achieve as problem size and physics complexity increase. We show that rigorously designed adaptive semi- and fully-discrete

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Helping computers to see who we really are

1 min read ·
Algorithms that train computers to automatically detect human activity in videos can improve online searches and real-world surveillance systems.

New Data Mining approach makes recurring patterns easier to be spotted

1 min read ·
The ultimate answer is ScaleMine, cost-effective and agile scalable parallel frequent subgraph mining in a single large graph. Panagiotis Kalnis, Professor of Computer Science (CS), and the team from the KAUST Extreme Computing Research Center (ECRC), under the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division signed this novel approach.

Blind matchmaking for more efficient wireless networks

1 min read ·
Autonomously pairing network users could expand the capability of the next generation of wireless networks

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Robots learn by checking in on team members

1 min read ·
Innovative drone designs and software enables a team of drones to work together in a coordinated approach.

A race against time

1 min read ·
The theme of the University's 2019 Winter Enrichment Program (WEP) was "time." It represented an opportune moment for KAUST to welcome members of the McLaren Group to campus to discuss the recently signed extreme performance research partnership between KAUST and McLaren.

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Teamwork pays off at startup Insyab

1 min read ·
Insyab, a technology startup specializing in smart solutions allowing robots and drones to collaborate on the execution of common tasks, resulted from three years of its founders' dedicated research at KAUST.

An algorithm for success: KAUST-designed differential equation-solving algorithm included in Maple 2020

1 min read ·
A KAUST-designed symbolic algorithm to solve nonlinear ordinary differential equations (ODEs) has been selected for inclusion in the current version of the computer algebra system (CAS) Maple 2020. The global symbolic and numeric computing software system—developed by the Canadian company Waterloo Maple (Maplesoft)—is widely used by scientists, engineers, and researchers to analyze, visualize, and solve mathematical problems.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Xun Qian

Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected

Sian-Jheng Lin

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
Postdoctoral Research Fellow, Electrical and Computer Engineering , King Abdullah University of Science and Technology. Project Researcher, University of Science and Technology of China (USTC), PRC.
<> nanofabrication (18)

Fei Xiang

Study of ultraviolet AlGaN nanowires light-emitting diodes

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B3 L5 R5209

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Liquid Metal-Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

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B2 B3 A0215

In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon counterparts and extend their applications.

KAUST PhD: UC Berkeley Best Research Award 2014

1 min read ·
KAUST CEMSE/EE graduate Dr. Hossain Fahad has just received the Best Research Award among 150 UC Berkeley research scientists, post-docs and graduate students.

PhD Student Hanan Mohammed wins PATW Competition

1 min read ·
Student wins the IET Saudi Arabia Young Professionals PATW Competition 2014/15.

CEMSE student gets Best Paper Award at 2DFDU2016

1 min read ·
CEMSE student Chun-Ho Lin won the Best Paper Award at the 2nd International Symposium on Devices and Application of Two-dimensional Materials (2016), held in Shanghai on June 20, 2016.

Crafting technology from nature's darkest secrets

1 min read ·
Exploiting the properties of disordered chaotic systems leads to low-cost energy harvesting and innovative micro-surgery applications.

Conducting Fab lessons in a virtual clean room

1 min read ·
A virtual platform makes learning how to fabricate a semiconductor device easier, safer and more interactive.

Meet KAUST prospective student: Dhanu Chettri

1 min read ·
Dhanu Chettri holds a master’s degree in electronic and communication engineering from Manipur University, India. Before joining KAUST, he worked as a senior project fellow at Council of Scientific and Industrial Research-Central Electronics Engineering Research Institute (CSIR-CEERI). Dhanu will join KAUST this fall as a Ph.D. candidate and member of the Advanced Semiconductor Laboratory under the supervision of Professor Xiaohang Li.

Smart memory devices that can sense

1 min read ·
In-memory sensing technology will facilitate electronic systems that are faster, smaller and consume less energy.

Ditching sensors for streamlined control of microdevices

1 min read ·
Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.

Wedyan Babatain

Wedyan Babatain is a doctoral candidate of MMH Labs in the Electrical Engineering program at KAUST. She has an interdisciplinary background obtaining her bachelor's degree in Biomedical Engineering from the University of Delaware, USA, in 2017, and her master's degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), KSA, in 2019. Her research interests include sensors, actuators, microfluidics, and flexible and soft electronics for healthcare and environmental applications. She was a finalist at the 2022 SXSW Innovation Awards and has presented her

Hossain Fahad

Hossain Fahad received both, his Master's and Ph.D. Degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Muhammad Mustafa Hussain. He is currently a post-doctoral researcher leading sensors research Prof. Ali Javey`s lab at EECS, UC Berkeley. He has authored more than 20 journal publications and his expertise is in the advanced device and sensor technology. Fahad is proficient in epitaxy, chemical vapor deposition, physical vapor deposition (sputtering and evaporation), atomic layer deposition, lithography, reactive ion

Hanan Mohammed

​ Hanan Mohammed obtained her Ph.D. in Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jurgen Kosel. Education Profile Ph.D., Electrical Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 2018. M.Sc, Nanoscale Science & Technology, University of Leeds, United Kingdom 2012. B.Sc, Physics, University of Calicut, Kerala, India, 2010. Selected Publications Mohammed, Hanan, et al. "Magnetotransport measurements of domain wall propagation in individual multisegmented cylindrical nanowires."

Hadi Amata

Hadi Amata is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Amata received his master degree in Electronics/Microelectronics from Provence University in France in 2008. He obtained a doctoral in Electrical Engineering “Optics and Photonics” from Jean Monnet University of Saint Etienne in France in 2012. He started his career life as a teaching assistant at Saint-Étienne Institute of Technology (IUT) in 2010. He was also a teaching

Dante Serrano Kobylyansky

Dante Serrano Kobylyansky is an MS student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), conducting research within the Integrated Microwaves Packaging Antennas & Circuit Technology Research Group under the supervision of Professor Atif Shamim. Educational Profile Dante obtained his bachelor's degree in Nanotechnology from the National Autonomous University of Mexico in June 2023. He joined KAUST as an Electrical and Computer Engineering student with the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group

Davide Priante

Davide Priante obtained his Ph.D. and Master's degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). Davide received his B.Sc. degree in Material Science Engineering from the University of Padova, Italy, in 2013. He joined KAUST in February 2014 as an M.S. to a Ph.D. student in Photonics Laboratory in the Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) Division. He received his MSc degree in August 2015 with a thesis based on the recombination mechanism of CH3NH3PbBr3 perovskite. Davide

Chun-Ho Lin

Chun-Ho Lin obtained his Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jr-Hau He in 2018. Lin completed his master’s degree in the Department of Electrical Engineering from the National Taiwan University and began his Ph.D. studies at KAUST in 2014. Research Interest Chun-Ho Lin's research interests include Perovskites, Nanophotonics, Flexible Electronics, Paper device, and 2D Materials.
<> Scientific Visualization (18)

Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

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B9 L2 R2322 H1

In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
avatar

Xingdi Zhang

Ph.D. Student, Computer Science
Visualization: Riemannian Geometry based observer relative vortex extraction; Computer vision: depth estimation, point cloud understanding. Computer graphics: discrete geometry, graphics engine, etc.

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Reem Alghamdi

Ph.D. Student, Computer Science
Reem Alghamdi is a Ph.D student in VCCVIS group under the supervision of Professor Markus Hadwiger. Education Profile MS in Computer Science, King Abdullah University of Science and Technology, Saudi Arabia. B.Sc. in Computer Science, Princess Nourah bint Abdulrahman University, Saudi Arabia.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Matej Mlejnek

Research Scientist, Computer Science
Matej Mlejnek is a Research Scientist in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his bachelor degree in Automation and Control from Slovak University of Technology in Slovakia in1999. After that, he joined Vienna University of Technology in Austria and received both his master (2003) and doctoral (2006) degrees in Computer Science. Research Interest Matej is interested in scientific visualization. Selected Publications Matej Mlejnek has many

Marco Agus

Marco Agus was a Research Engineer in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his master degree in Electronic Engineering from the University of Cagliari in Italy in 1999. He has a Ph. D. in Mechanical Design from the University of Cagliari. Before joining KAUST, Marco was a researcher at Visualization and Virtual Reality Group in the Center for Advanced Studies, Research and Development in Sardinia (CRS4) in Italy. After that in 2007, he moved to

Hisham Alhussain

Hisham Alhussain is a Computer Science Master Student under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hisham received his bachelor's degree in Computer Science from the University of Massachusetts at Amherst in 2018. He is also a Software Developer at IBM. Research Interest He's currently focusing on Scientific Visualization and Visual Computing. He is proficient in Python, Java, and has professional experience in React/React-Native and mobile and web development

Haneen Mohammed

Haneen Mohammed was a Master student in Computer Science Program under the supervision of Professor Markus Hadwiger at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, she’s preparing her doctoral in Computer Science at Columbia University in the City of New York, United States. Education and Career Haneen earned her bachelor degree in Electrical and Computer Engineering from Effat University in Jeddah in 2015. She joined KAUST and received her master degree in Computer Science in 2017. Research Interest ​Her research interests are mainly

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Diego Rojas Blanco

Diego Rojas is a Ph.D. candidate in Earth Science & Engineering. He is studying under the supervision of Professor Matteo Parsani. Diego works on the development of entropy stable algorithms for the simulation of turbulent flow. Algorithms of this kind have new and exciting properties related to the reliability and scalability of our simulations so that we can use the power of modern supercomputers to simulate fluid flow problems to higher resolution. Research Interest Diego Rojas research interest include Computational Fluid Dynamics (CFD), Scientific Visualization, and High Performance

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.
<> semiconductors (18)

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Design and Process Development of Graphene-Based Geometric Diodes for Enhanced Performance

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B2 L5 R5220

This thesis advances graphene geometric diodes (GGDs) by addressing fabrication, performance, and design challenges using innovative nanofabrication techniques and artificial intelligence-enhanced computational methodologies.
optoelectronic device can adapt to light

Artificial neurons enable neuromorphic computing with light

1 min read ·
An adaptive capacitor that can sense and learn through optical stimuli enables learning and recognition tasks.

Study of III-nitride Nanowire Growth and Devices on Unconventional Substrates

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B3 L5 R5209

This thesis aims to investigate the microscopic characteristics of the nanowires and expand on the possibility of using transparent amorphous substrate for III-nitride nanowire devices. In this work, we performed material growth, characterization, and device fabrication of III-nitride nanowires grown using molecular beam epitaxy on unconventional substrates including silicon substrates and fused silica substrates. We also investigated the effect of various nucleation layers on the morphology and quality of the nanowires.

Hybrid integration of materials for ultraviolet detection

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B9 L2 H1 R2322

Semiconductors are pervasive in consumer electronics and optoelectronics, and the related optical devices are deemed disruptive that Nobel Prize in Physics in 2014 was awarded to the inventors of blue light-emitting diodes (LEDs), which “has enabled bright and energy-saving white light sources”. While AlInGaN-based lasers and LEDs, and silicon-based photodetectors are currently matured, unconventional usage based on the materials has demonstrated their further potential, including solar-hydrogen generation, indoor-horticulture, and high-speed communication.

The fourth wave: ultrawide bandgap compound semiconductors for photonics and electronics

Xiaohang Li, Assistant Professor, Electrical and Computer Engineering

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KAUST

Wide bandgap (WBG) compound semiconductors including GaN have shown enormous success in solid-state lighting, display, and electrification in recent decades due to superior properties such as direct bandgap, high electron mobility, and large breakdown field. They have been changing the world by elevating living standards and addressing grand challenges such as global warming. The pioneering researchers have been recognized by numerous accolades including the Nobel Prize and most recently, the Queen Elizabeth Prize. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) compound semiconductors with bandgap larger than 3.4 eV have attracted increasing attentions: they have been regarded as the 4th wave/generation after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, respectively, both crucial for human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. However, extensive multi-disciplinary studies of growth, materials, physics, and devices are essential to unearth the potentials due to the infancy. This seminar would cover the latest research on those aspects. It includes growth of state-of-the-art materials, discovery of unique material properties, and development of a widely adopted device physics framework for photonics and electronics especially short and long wavelength photonic devices.

Building crystals on a very hot surface

1 min read ·
An innovative chemical reactor for depositing semiconductors at very high temperatures draws on the strength of Saudi Arabia’s chemical industry.

Controlling the crystal structure of gallium oxide

1 min read ·
Precise control of the atomic structure of gallium oxide layers improves the development of high-power electronic devices.

KAUST Professor Kazuhiro Ohkawa elected Fellow of the Japan Society of Applied Physics

1 min read ·
By David Murphy, KAUST News KAUST Professor of Electrical Engineering Kazuhiro Ohkawa was recently elected as a Fellow of the Japan Society of Applied Physics (JSAP). The JSAP was established in 1930 as a voluntary forum for applied physics researchers in the country. Ohkawa's JSAP peers recognized him for his exceptional contributions to the progression of applied physics and his specific contribution to the "development of wide bandgap semiconductor epitaxial growth and optical devices," JSAP stated. "It is a great honor for me to be elected as a Fellow of the JSAP," Ohkawa noted. "The

Meet KAUST prospective student: Haoliang Lu

1 min read ·
Haoliang Lu, 22, is an electrical engineering graduate who will join KAUST from the University of Electronic Science and Technology, China. Haoliang will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate, and member of the Integrated Circuits and Systems (ICS) research group, under the supervision of Professor Hossein Fariborzi.

Meet KAUST prospective student: Jose Manuel Taboada Vasquez

1 min read ·
Jose Manuel Taboada Vasquez is a B.Sc. graduate in nanoscience and nanotechnology from the National Autonomous University of Mexico (UNAM), Mexico. He will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate, and member of the Advanced Semiconductor Laboratory under the supervision of Professor Xiaohang Li.

Meet KAUST prospective student: Zhiyuan Liu

1 min read ·
Zhiyuan Liu, 22, is an electrical engineering graduate from the University of Electronic Science and Technology of China, China. Zhiyuan will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Advanced Semiconductor Laboratory under the supervision of Professor Xiaohang Li.

Meet KAUST prospective student: Dhanu Chettri

1 min read ·
Dhanu Chettri holds a master’s degree in electronic and communication engineering from Manipur University, India. Before joining KAUST, he worked as a senior project fellow at Council of Scientific and Industrial Research-Central Electronics Engineering Research Institute (CSIR-CEERI). Dhanu will join KAUST this fall as a Ph.D. candidate and member of the Advanced Semiconductor Laboratory under the supervision of Professor Xiaohang Li.

Making the shift from blue to red for better LEDs

1 min read ·
Pure red-light micrometer-scale emitting devices made from a nitride semiconductor reaches excellent efficiency.

Ram Chandra Subedi

Ram Chandra Subedi (RC) is a Ph.D. student at Photonics Lab under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). He received his B.Sc. and M.Sc. in Physics from Tribhuvan University, Nepal in 2009 and 2011 respectively. In addition, he received his M.S. in the Experimental Condensed Matter Physics University of Georgia, Athens (UGA in 2016). During his stay at UGA, he worked as a Teaching/Research Assistant. His research at UGA was primarily focused on polymer/ small molecule-based Organic Light-Emitting Diodes (OLEDs) and Organic Spin

Heng Wang

Ph.D. student in Electrical and Computer Engineering at KAUST with expertise in nanofabrication, electron beam lithography, and nanodevices. Focused on graphene-based geometric diodes.

Haoliang Lu

Haoliang Lu is an M.S./Ph.D. candidate in the KAUST Integrated Circuits and Systems (ICS) research group, under the supervision of Professor Hossein Fariborzi. Before joining KAUST, Haoliang obtained a bachelor's degree in electrical engineering from the University of Electronic Science and Technology of China. Research Intterests Haoliang's research interests mainly focus on magnetic materials and semiconductor materials.

Davide Priante

Davide Priante obtained his Ph.D. and Master's degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). Davide received his B.Sc. degree in Material Science Engineering from the University of Padova, Italy, in 2013. He joined KAUST in February 2014 as an M.S. to a Ph.D. student in Photonics Laboratory in the Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) Division. He received his MSc degree in August 2015 with a thesis based on the recombination mechanism of CH3NH3PbBr3 perovskite. Davide
<> AI (17)

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Rising stars, global impact: KSA at the forefront of AI innovation

1 min read ·
KAUST showcases global AI talent to power Saudi Arabia’s rise as a hub for innovation and leadership in artificial intelligence.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.
Rising Stars in AI" Symposium 2025

Rising Stars in AI Symposium 2025

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Building 19, Halls 1, 2 and 3

AI

Join KAUST's Rising Stars in AI Symposium 2025, bringing together exceptional emerging talent shaping the future of AI research and innovation.

KAUST developing robotic system to improve date palm harvesting

1 min read ·
Combination of AI and robotics positions Saudi Arabia to transform date farming.
kaust-iit-collaboration-life-sciences-nanotechnology-research

KAUST collaboration to benefit life sciences and nanotechnology research

1 min read ·
KAUST and IIT unite to enhance research in life sciences, AI, robotics and nanotech, driving innovation through collaboration.
optoelectronic device can adapt to light

Artificial neurons enable neuromorphic computing with light

1 min read ·
An adaptive capacitor that can sense and learn through optical stimuli enables learning and recognition tasks.
AI tool maps hidden links between diseases

AI tool maps hidden links between diseases

1 min read ·
Understanding how diseases trigger one another could lead to better treatments, prevention strategies and improved genetic risk prediction.
Olga Krestinskaya

International recognition for KAUST Ph.D. candidate Olga Krestinskaya

3 min read ·
Olga Krestinskaya, a KAUST Ph.D. student, wins Best Poster at the 2024 Nature Neuromorphic Computing Conference for her work on energy-efficient AI hardware.
Sustainable solutions in water conservation

KAUST-Engineered tool drives sustainable solutions in water conservation

3 min read ·
KAUST’s startup EcoStream cuts water waste by 50% while promoting affordability and sustainability, winning the 2024 StartSmart prize.

Aznaur Aliev

M.S. Student

Mhd. Emad Alolaby

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Omar Khalifa

PhD student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST) ,working under the supervision of Prof. Tareq Al-Naffouri.

Hamzeh Abu Qamar

MS Student, Electrical, Electronics and Communications Engineering at King Abdullah University of Science and Technology (KAUST).
<> Cognitive radio network (17)

Physical Layer Security of Wireless-powered NOMA-based Cognitive Radio Networks: Fundamentals & Challenges

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B1 L2 R2202

Abstract In the last decade, with the emergence of the internet of things (IoT) as well as machine-to-machine (M2M) paradigms, future wireless networks are targeting new critical services such as remote healthcare, surveillance, transportation, etc. In this regard, with the massive expected number of connected IoT and M2M devices, several requirements have been imposed by the regulatory bodies onto the deployment of 5G network. These requirements include, but not limited to, higher connectivity, reduced latency, as well as increased spectral and energy efficiency. Interestingly, the

EE PhD Candidate Fahd Ahmed Khan from Prof. Alouini's group wins the best poster award at the IEEE DySPAN'2012

1 min read ·
Fahd Khan, a Ph.D. candidate in the Electrical Engineering (EE) Program at the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) division received the best poster award at the IEEE DySPAN'2012.

Class 2013 Electrical Engineering PhD graduates

1 min read ·
The Electrical Engineering Faculty, Staff and students would like to extend the warmest congratulations to the class of 2013 Ph.D. graduates. We wish them success in the new phase of their lives and are sure they will be our best ambassadors to the whole world.

Dr. Hesham ElSawy Elevated to Senior Member of IEEE and Recognized as an Exemplary Reviewer by the IEEE Transactions on Communications

1 min read ·
Dr. Hesham ElSawy has been elevated to the rank of Senior Member of IEEE, the world's largest technical professional organization for the advancement of technology. The achievement is considered a significant honor since it is granted only to researchers who have shown a significance and continuous performance over a period of five years in professional practice. At present, only an 8% of 400,000 IEEE members have received this honor because of their superior professional achievements.

Hesham ElSawy wins IEEE Communication Society

1 min read ·
The Internet of Everything in quickly growing and vastly enlarging its spectrum of applications. At the same time, traffic demand per-user is increasing dramatically. It is estimated that by 2020 there will be more than 50 billion devices connected. The cellular support infrastructure should be developed accordingly, making of network performance analysis a critical task. The existing elementary probability methods are currently inefficient when it comes to detecting mobility faults and interference.

Hesham ElSawy gains ISWCS Best Scientific Contribution Award 2017

1 min read ·
The imminent era of the smart world is foreseen to involve massively many power and computationally constraint wireless devices with sporadic traffic patterns. The surging Internet of things (IoT) and cyber physical systems (CPS) are clear examples that rely on massive wireless networks. Hence, realizing such extensive wireless connectivity is mandatory to unlock the potentials and reap the benefits of the foreseen smart era.

Weal Ghazy Alheadary

Introduction Weal Ghazy Alheadary is an Assistant Professor at Taibah University, Medina, Saudi Arabia Education and early career Weal Ghazy obtained his B.S degree in Computer Engineering, King Saud University of Science (KSU), Riyadh, Saudi Arabia in 2004. After a while in 2010, he got his M.S degree from the same university. In 2013 he joined King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia as a Ph.D. student in Electrical Engineering. Research Interests Weal is mainly interested in the following areas: ​Cooperative relay network Cognitive radio network. MPLS

Taniya Shafique

Taniya Shafique was a ​Visiting Student in the CTL from Texas A&M University at Qatar. Selected Publications ​Shafique, M. Zia, H. D. Han, and H. Mehmood, " Cross-layer Chase combining with selective retransmission, analysis, and throughput optimization for OFDM Systems,''. IEEE Transactions on Communications, vol. 64, no. 6, pp. 2311-2325, June 2016. Awards ​Gold medal in M.Sc. Gold medal in M.Phil.

Sultangali Arzykulov

Postdoctoral Research Fellow, Electrical and Computer Engineering
Sultangali Arzykulov(Member, IEEE) received the B.Sc. degree (Hons.) in radio engineering, electronics and telecommunications from Kazakh National Research Technical University after K. I. Satpayev, Almaty, Kazakhstan, in June 2010, the M.Sc. degree in communication engineering from The University of Manchester, Manchester, U.K., in 2013, and the Ph.D. degree in science, engineering and technology from Nazarbayev University, Nur-Sultan, Kazakhstan, in 2019. He is currently a Postdoctoral Scholar with the Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah

Redha M. Radaydeh

Research Scientist, Communication Theory Lab
From Sept. 2009 to Sept. 2012, Redha M. Radaydeh was a research fellow at KAUST and then an associate research scientist at TAMU Qatar. From Sept. 2012 to Aug. 2016, he worked at Alfaisal University as an associate professor of EE. Research Interests • Overlaid multi-tier HetNets. • Performance analysis of UA communications. • Resource sharing, distribution, and allocation techniques. • Interference management and mitigation schemes. • D2D and small-cell communications in 5G cellular networks. • Mixed RF and FSO communications. • Physical layer security. • Cooperative relaying systems. •

Nurul Huda Mahmood

Nurul Huda Mahmood is a Scientific Assistant at Aalborg University (AAU), Aalborg, Denmark. Education Nurul Huda obtained his M.S. in Mobile Communications, Department of Electronic Systems, Aalborg University (AAU), Denmark in 2007. In 2012, he got his Ph.D. in Communication Theory, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. During his Ph.D. degree, he came to KAUST from January 2011 to June 2011 as a Visiting Ph.D. student. Research Interests Cooperative relay network Cognitive radio network Interference aware MIMO transmitter and receiver design Education

Madi Aldabergenov

Madi Aldabergenov was a Visiting B.S. Student in CTL. Education B.S. in Electrical and Electronics Engineering, Nazarbayev University, Astana, Kazakhstan (September 2011 - June 2016). Scientific and Professional Memberships IEEE Student Member. Education Profile B.S. in Electrical and Electronics Engineering, Nazarbayev University, Astana, Kazakhstan (September 2011 - June 2016).

Ju-Hyung Lee

Introduction Ju-Hyung was visiting student at KAUST during his Ph.D. degree. Education ​Master-Ph.D. Integrated Course in School of Electrical Engineering, Korea University M.S. in Ju-Ju-Hyung obtained his B.S. degree in Electrical Engineering, Korea University, Seoul, Korea in 2015. After he started ​Master-Ph.D. Integrated Course in School of Electrical Engineering, Korea University M.S. in Electrical Engineering, Korea University, Seoul, Korea. During his studies, he came to KAUST as a visiting Ph.D. student. Rese arch Interests ​Free-space optical wireless communication Trajectory

Jia Ye

Jia Ye is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jia Ye obtained her bachelor degree in communication engineering from Southwest University, Chongqing, China, in 2018. Research Interest Jia Ye is focusing in the area of wireless communication. She’s currently working on system performance evaluation of wireless communication systems. Selected Publications J. Ye, G. Pan and M. -S. Alouini, " Earth Rotation-Aware Non-Stationary

Hesham ElSawy

Postdoctoral Research Fellow, Communication Theory Lab
From April 2014 to June 2018 Hesham ElSawy was a postdoctoral fellow in the Communication Theory Lab (CTL), at the Computer, Electrical, and Mathematical Sciences and Engineering division (CEMSE), King Abdullah University for Science and Technology (KAUST), Kingdom of Saudi Arabia. He is now an assistant professor in King Fahd University of Petroleum and Minerals (KFUPM), Eastern Province, KSA Selected Publications Ph.D. Thesis H. ElSawy, “Modeling, Analysis, and Optimization of Random Wireless Networks: Stochastic Geometry Approach,” University of Manitoba, Winnipeg, MB, Canada, March 2014. M

Amal Hyadi

Introduction Amal Hyadi was a Ph.D. Student. June 2013-September 2017 at KAUST and she went to McGill University of Quebec, Canada as a Postdoctoral Research Fellow after graduation. Education and Early Career Amal obtained her degree in Diplome d’Ingénieur, Institut Nationale des Postes et Télécommunications (INPT), Rabat, Morocco in 2011. She was a visiting student at KAUST from INPT Rabat between February 2011 and June 2011. After graduation, she joined the MS program in Electrical Engineering, KAUST, Saudi Arabia. Research Interest Amal is mainly interested in Cooperative relay networks

Ahmed Sultan Salem

Instructional Professor, Electrical and Computer Engineering
Instructional Professor of Electrical and Computer Engineering Ahmed K. Sultan has extensive experience teaching at graduate and undergraduate levels in topics such as wireless communications and mathematics for signal processing and digital communications.
<> PDE (17)

Part 1: Introduction to homogenisation and Gamma-Convergence

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B2 B3 A0215

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 1: (1) The homogenisation problem; (2) Gamma-convergence - definition, main properties, examples, comparison with pointwise limit; (3) Lower semicontinuity, lower semicontinuous envelope; (4) Coercivity

Part 2: Introduction to homogenisation and Gamma-Convergence

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B1 L2 R2202

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 2: (1) Integral functionals with p growth, 1 p 1; The localisation method of Gamma-convergence; Integral representation.

Part 3: Introduction to homogenisation and Gamma-Convergence

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B1 L4 R4214

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 3: (1) Compactness; (2) Application of the compactness result; 93) The homogenisation theorem.

A very short introduction to regularity theory for linear elliptic equations - Part 1

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 3

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 4

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 2

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L2 R2202

Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Unveiling the potential of energy/entropy stable numerical methods for hyperbolic/mixed hyperbolic-parabolic PDEs

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B9 L2 H1

Abstract The demand for increasingly multidisciplinary reliable simulations, for both analysis and design optimization purposes, requires transformational advances in individual components of future PDEs solvers. At the algorithmic level, hardware compatibility and efficiency are of paramount importance in determining viability on future hardware. However, equally important (if not more so) is provable algorithmic robustness which becomes progressively more challenging to achieve as problem size and physics complexity increase. We show that rigorously designed adaptive semi- and fully-discrete

Deterministic particle approximation for nonlinear transport equations

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Building L3 R3119

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

WAN Approximation of PDEs: Best Approximation, Stabilization and Boundary Conditions

Prof. Silvia Bertoluzza

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B2 L5 R5209

We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.

Virtual Element Approximation of Eigenvalue Problems

Prof. Francesca Gardini, Università di Pavia

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B1 L3 R3119

We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.

KAUST Ph.D. student Lukas Larisch wins the PACE 2017 Challenge

1 min read ·
Lukas Larisch, a KAUST Ph.D. student in Computer Science working under the supervision of Professor Gabriel Wittum, won the PACE 2017 Parameterized Algorithms and Computational Experiments Challenge. The award ceremony that took place in Vienna, Austria, at the beginning of September this year has been held during the ALGO Congress at the International Symposium on Parameterized and Exact Computation (IPEC 2017).

Myoungkeun Shin

PDE UWOC

Myoungkeun Shin is a MS/Ph.D student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Myoungkeun obtained his bachelor degree in mathematics from Seoul National University (SNU) in 2011. Before joining KAUST, he taught mathematics to high school students in Korea. Research Interest Myoungkeun’s primary research interests are in PDE and UWOC. Education Profile M.S. in Applied Mathematics and Computational Science, Kind Abdullah University of Science and

Lukas Larisch

Ph.D. Student, Computer Science
Lukas Larisch is a Phd candidate in Computer Science. He is studying under the supervision of Professor Gabriel Wittum. Lukas has a bachelor degree in Computer Science from the Goethe University of Frankfurt, Germany. Education Profile B.Sc. in Computer Science from the Goethe University of Frankfurt, Germany. Awards and Distinctions Winner of the PACE 2017 Challenge

Johannes Schneider

Research Scientist, Modelling and Simulation
Johannes Schneider was a consultant working with Professor Gabriel Wittum. His research focused on modeling and simulation of biogas plants. Education Profile Master in Electrical Engineering, Goethe University Frankfurt, Germany Diploma in Engineering Areas of Expertise and Research Interests Potential emissions from biogas-sludge storage tanks

Dmitry Logashenko

Research Scientist, Modelling and Simulation
Research Interests Dr. Logashenko is working in the field of the numerical simulation of physical processes. His interests concern development, analysis and implementation of efficient methods for treating the (integro-) partial differential equations on parallel architectures. Main fields of the applications are groundwater flows in fractured porous media and CFD. He also collaborates in the ug4 toolbox project as a developer. Professional Memberships Research scientist at the Computer, Electrical and Mathematical Sciences and Engineering Division of the King Abdullah University of Science

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.
<> supercomputing (17)

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

Timothy Lanfear , Brent Leback

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B4 B5 A0215

The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Device accelerated solvers for PDEs. Current status and future perspectives

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B5 L5 R5220

In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

New Data Mining approach makes recurring patterns easier to be spotted

1 min read ·
The ultimate answer is ScaleMine, cost-effective and agile scalable parallel frequent subgraph mining in a single large graph. Panagiotis Kalnis, Professor of Computer Science (CS), and the team from the KAUST Extreme Computing Research Center (ECRC), under the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division signed this novel approach.

KAUST gets ready for SC18

1 min read ·
For the past 10 years, KAUST has made a strong presence at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) that takes place in different cities around the U.S. every year. 2018 will mark a special year for KAUST at SC, which will take place in Dallas, Texas, in November, with around 13,000 supercomputing professionals in attendance. The University will have an engaging presence on the exhibition floor and in the seminars, and the technical program of SC18 will be chaired by the Director of the KAUST Extreme Computing Research Center (ECRC)

Next generation algorithm advances machine learning of powerful supercomputers

1 min read ·
A multi-disciplinary team of international researchers from KAUST and Japan's National Institute of Informatics (NII) in collaboration with U.S. supercomputer company Cray Inc. successfully implemented a new algorithm to efficiently harness the computational power of the fastest supercomputers in the world. "Software integration is ultimately the icing on the cake for advanced computational projects. Thanks to Cray's support, our algorithms are now deployed on all Cray supercomputers, including KAUST's Shaheen II supercomputer, as well as half of the top 10 fastest supercomputers in the world

Putting the sense in materials

1 min read ·
An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.

KAUST Professor David Keyes to chair International Supercomputing Conference 2020

1 min read ·
David Keyes, KAUST professor of applied mathematics and computational science; director of the University's Extreme Computing Research Center; and senior associate to the President of KAUST, will chair the International Supercomputing Conference (ISC) 2020. He will be the first program chair from a Middle Eastern institution, as announced by ISC 2020.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Shaheen II open to serve COVID-19 research around the Kingdom

1 min read ·
The COVID-19 pandemic has imposed challenges across the globe that need to be solved using scientific discovery and innovation. In line with that, the KAUST Supercomputing Core Laboratory (KSL) has opened a call for proposals that require its supercomputer Shaheen II, as well as other cluster computing resources, to support research projects.

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

Mixing precision for model acceleration

1 min read ·
A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

KAUST presents exascale expertise at SC21

1 min read ·
Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non
kaust-cemse-panagotis-kalnis

Panagiotis Kalnis

Professor, Computer Science
Professor Panos Kalnis is a leading researcher in big data, cloud computing, parallel and distributed systems and computing privacy for data mining and bioinformatics.
<> antenna arrays (16)

Prof. Khaled Salama appointed Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs

1 min read ·
Prof. Khaled N. Salama was appointed Associated Editor for IEEE Transactions on circuits and systems-II: Express Briefs (TCAS II) for the period 2014-2015.

Prof. Khaled Salama's Work On Fractional Order Element Based Impedance Matching, Granted US Patent US 8762114

1 min read ·
Prof. Khaled Salama in collaboration with Dr. Ahmed Radwan and Prof. Atif Shamim obtained a US Patent

Muhammad Fahad Farooqui and Ahmed Nafe's paper selected as best paper

1 min read ·
Paper by KAUST students Muhammad Fahad Farooqui and Ahmed Nafe from Prof. Atif Shamim's group has been selected as the best paper in the "Inkjet-Printed Components category".

A novel design of antenna array developed by KAUST scientists

1 min read ·
At the sensors lab, Prof. K.N. Salama and Prof. A. Shamim led a team of KAUST founding masters students Farhan Abdul Ghaffar and Muhammad Umair Khalid to develop an aperture coupled patch antenna array SoP.

This is your light speaking, how can I help you?

1 min read ·
Prof. Salama's Smart Lighting System won the 1st prize at WEP Science Fair with a device that will change the way we interact with light.

New low cost diagnostic for cardiovascular risk detection

1 min read ·
A new flexible, low-cost cardiovascular risk marker biosensor was developed by Prof. Khaled Salama and his team at KAUST University, Saudi Arabia.

Professor Khaled Salama awarded US patent on MEMS fractal capacitors

1 min read ·
Patent application US 20140239446 A1 on "Fractal Structures For Fixed MEMS Capacitors" was awarded a USA patent.

Three new patents for Professor Khaled Salama and the Sensors Lab Team

1 min read ·
With the latest project on Digital Chaos Systems, KAUST's Associate Professor of Electrical Engineering Khaled Salama and the Sensors-Lab's team (http://sensors.kaust.edu.sa/research/current/digital-chaos-systems) secured stunning achievements and three key patents, opening new frontiers for cybersecurity, cryptography, and electronic devices.

Prof. Salama's paper hits the top for 6 months in the row!

1 min read ·
In August 2016, Prof. Salama's article "Stochasticity Modeling in Memristors", was for the sixth time consecutively among the most downloaded paper of the month and the most read article in the IEEE Transactions on Nanotechnology (TNANO).

Flash Microfluidics, rapid Lab on a Chip fabrication method for cost- effective in situ production.

1 min read ·
From DNA sequencing to environment monitoring, microfluidics devices have been broadly employed, but their production is more and more time-consuming and expensive. Researchers from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, have developed an innovative prototyping method for Lab on a Chip (LOC) production to plummet costs while augmenting performances.

KAUST-born robotic avatar sets new era for undersea archaeology

1 min read ·
In 2015, an extensive archaeological programme led by the French Ministry of Culture and the University of Montpellier set up a scientific collaboration with Khaled Nabil Salama, Professor from CEMSE Division and co-worker Professor Christian R. Voolstra, from the KAUST Red Sea Research Center, in partnership with Stanford University and Meka Robotics, in California, to develop Ocean One, an agile avatar that affords immediate and haptic-visual interaction – the process of recognizing objects through touch - in the ocean environment at depths greater than 50m.

KAUST faculty members receive the King Prize for Honoring Inventors and the Gifted

1 min read ·
KAUST Professors Atif Shamim and Osman Bakr received the Custodian of the King Prize for Honoring Inventors and the Gifted, which was organized by King Abdulaziz City for Science and Technology (KACST).

KAUST named among academic winners at the 2020 Ansys Hall of Fame Competition

1 min read ·
Landmine detection research developed by KAUST scientists—Sebastian Celis Sierra, Li Zhang, and Mohamed Farhat—was named among the top three academic winners of the 2020 Ansys Hall of Fame competition. Based in Pennsylvania, U.S., Ansys develop and market multiphysics engineering simulation software for product design.

Khaled Salama

Professor Salama is a prominent leader in the development of intelligent sensors, biosensors and very large-scale integration (VLSI) architectures for bioimaging and instrumentation. He is one of the founders of the Electrical and Computer Engineering (ECE) Program at KAUST.

Kirill Klionovski

Kirill Klionovski was a Postdoctoral Fellow working under the supervision of Professor Atif Shamim at Integrated Microwaves Packaging Antennas and Circuits Technology (IMPACT) Research Group in King Abdullah University of Science and Technology (KAUST). Currently, he is working as an Antenna Researcher in KREEMO company, Seoul. Research Interests Kirill's research interests include ​Terahertz antennas, 5G antennas, and antenna arrays. Education Profile ​Ph.D. in Radiophysics, Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Science, 2015​.

Ahmed Nafe

Ahmed Nafe obtained Ms degree in electrical engineering under the supervision of Prof. Atif Shamim from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2014. He received the B.Sc. degree (Hons.) in electrical engineering from the German University in Cairo (GUC), Cairo, in 2012. His B.Sc. thesis concerned the design and characterization of millimeter-wave antenna arrays for automotive radars with Ulm University, Ulm, Germany. He is currently pursuing a Ph.D. degree in electrical and computer engineering at the University of California at San Diego, La Jolla
<> CMOS (16)

Redesigning CMOS Electronics: What, Why and How?

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KAUST

A little more than half of the world’s population enjoy benefits of information technology which is enabled by complementary metal oxide semiconductor (CMOS) electronics. Going forward, we will enjoy further augmentation of quality of life through integrated CMOS electronic systems consisting of logic, memory, communication devices, energy storage and harvester, power management units, sensors and actuators. Their main attributes will include but not limited to high performance and storage capacity for data management; seamless connectivity; energy efficiency; hyper-scale integration density; appropriate functionalities based on their applications and operational environment; reliability and safety; and finally affordability and simplicity to expand their user base to include those who do not have any access to them today. Even using last fifty years’ wealth of knowledge and experience, such integrated electronic system development and deployment is a monumental engineering challenge. From that perspective, redesigning CMOS electronics might seem to be an overly ambitious goal specially, if that means transformation of such physically rigid complex electronic systems into a fully flexible one. To address this intriguing challenge, we have developed a unique coin like architecture based soft singular platform, which can be used as the building block of standalone fully flexible CMOS electronic system with all the aforementioned characteristics. We have devised an effective heterogeneous integration strategy based on mature and reliable CMOS technology only to integrate hybrid materials and diverse set of devices for multi-disciplinary applications. These will be the focus of this talk.

Prof. Muhammad Hussain selected as distinguished lecturer for the IEEE Electron Devices Society

1 min read ·
Prof. Muhammad Hussain has been selected as a distinguished lecturer for the IEEE Electron Devices Society (EDS). The IEEE sponsored EDS Distinguished Lecturer Program exists for the purpose of providing IEEE EDS Chapters with a list of quality lecturers.

Prof. Hussain named Fellow of Institute of Nanotechnology, UK

1 min read ·
Dr. Muhammad Mustafa Hussain an Assistant Professor of Electrical Engineering from founding batch faculty has been named as the Fellow of Institute of Nanotechnology, UK.

KAUST team achieves remarkable flexibility with Silicon

1 min read ·
KAUST's Integrated Nanotechnology Lab are working to transform cutting-edge materials to make high-performance devices, but also about portability - they will have to be flexible, stretchable and reconfigurable

Stretchable Electronics Make Thermotherapy Easier for Arthritis Patients

1 min read ·
A paper has been published to cover a new technology that makes electronic devices stretchable while retaining their electrical and thermal properties.

Scientists made cutting edge 'smart skin' with household items

1 min read ·
Congratulations to Prof. Muhammad Hussain whose smart skin project made out of cheap household materials such as foil and tape - was featured on the Washington Post and a few other news media.

The Institute of Physics (IoP), nominated Prof. Muhammad Mustafa Hussain for the Fellowship at the Institute of Physics

1 min read ·
Prof. Muhammad Mustafa Hussain nominated for the Fellowship at the Institute of Physics (IoP), UK.

Prof. Muhammad Hussain's article selected for "2015 Applied Physics Letters Editors' Picks

1 min read ·
Prof. Muhammad Hussain's article "Functional integrity of flexible n-channel metal-oxide-semiconductor field-effect transistors on a reversibly bi-stable platform" was selected for "2015 Applied Physics Letters Editors' Picks"- published by the American Institute of Physics - a scientific journal featuring concise, up-to-date reports on significant new findings in applied physics.

Muhammad Hussain elected Fellow of the American Physical Society (APS)

1 min read ·
The Fellowship is an international distinction assigned only to highly selected scientists for their contributions in the fields of APS, and was given to Prof Hussain for contributions to exploration, evaluation, and transition of planar and nonplanar high-k/metal gate complementary metal oxide semiconductor electronics, silicon/silicongermanium/ III-V nanotube devices, and flexible, stretchable, reconfigurable complementary metal–oxide–semiconductor electronic systems.

Self-destructible electronics is a Mission Impossible no more

1 min read ·
In today's digital age, the increasing dependency on information makes everyone potentially exposed to privacy invasion and cyber security. Annual reports on tech-safety breaches are alarming, with stolen portable devices, laptops or mobiles, along with hacked hardware skyrocketing to 50% in the United States only. Bigger the picture, global is the risk of unwilling access to private lives or strategical documents, with everyone in the eye of the most dangerous of information violation. In such cases, an immediate ability to remotely destroy the sensitive parts, if it is not possible to regain them, would save us.

Costless and reliable 3D high-speed printing process to revolutionize decal sensors fabrication

1 min read ·
Attempts for 3D fabrication of decal electronics were previously made. But, rigid, bulky, and planar CMOS electronics were larger in size and limited in adaptability with the human body. Unwillingly, such obsolete device creates localized heating, power-loss and grumpy data performances.

Dental braces get smart

1 min read ·
-By Francesca Serra Muhammad Hussain, associate Professor of electrical engineering at the Computer, Electrical and Mathematical Science and Engineering Division of KAUST, recently developed a new dental brace technology that can straighten teeth faster and more effectively than actual products. The orthodontic system was designed to induce faster bone regeneration and to enhance protection of the enamel. According to Hussain and his team, the technology is going to introduce a paradigm shift in orthodontic treatments "The integration of high performance inorganic mechanically conformable and

Democratizing electronics one step at a time

1 min read ·
"You would not believe how many amazingly talented people there are in the world, but they often are just not exposed to opportunities," noted Muhammad Mustafa Hussain, KAUST professor of electrical engineering and currently a visiting professor at the University of California, Berkeley. He is determined to try and change this—one step at a time.

Professor Hussain elected IEEE Fellow

1 min read ·
Muhammad Mustafa Hussain, KAUST professor of electrical engineering, was recently elected as an Institute of Electrical and Electronics Engineers (IEEE) Fellow, effective January 1, 2020, for his “contributions to flexible and stretchable electronic circuits.

Taking engineering to the masses 

1 min read ·
KAUST, like many other international research institutions, is no different in its approach to addressing our current need for continuous and uninterrupted distance learning. E-learning has proved effective in theoretical and mathematically-oriented disciplines, where access to a computer locally or over the cloud is all that is required.

Hammad Mehmood Cheema

Hammad Mehmood Cheema worked as a Post Doctorate Fellow at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group with Professor Atif Shamim at King Abdullah University of Science and Technology (KAUST) during (2011-2013). Currently, Hammad is working as an Associate Professor at the National University of Sciences and Technology (NUST). Ph.D. Thesis: Hammad Mehmood Cheema, “Flexible Phase-Locked Loops and Millimeter Wave PLL Components for 60-GHz Wireless Networks in CMOS,” A catalogue record is available from the Eindhoven University of Technology Library, ISBN
<> computational imaging (16)

Professor Wolfgang Heidrich elected as a Fellow of the National Academy of Inventors and named a 2025 Optica Fellow

5 min read ·
KAUST Professor Wolfgang Heidrich has been named a Fellow of both the National Academy of Inventors (NAI) and Optica, recognizing his pioneering contributions to computational imaging, display systems and AI-driven optical technologies.

Space-Time Tomographic Reconstruction of Deforming Objects

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B1 L2 R2202

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

Optimization and Learning in Computational Imaging

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B9 H1 R2322

The Machine Learning Hub Seminar Series presents “Optimization and Learning in Computational Imaging” by Dr. Wolfgang Heidrich, Professor in Computer Science at KAUST. He leads the AI Initiative and is the Director of the KAUST Visual Computing Center. Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Historically, many such systems have employed simple transform-based reconstruction methods. Modern optimization methods and priors can drastically improve the reconstruction quality in computational imaging systems. Furthermore, learning-based methods can be used to design the optics along with the reconstruction method, yielding truly end-to-end learned imaging systems, blurring the boundary between imaging hardware and software.

Jinhui Xiong wins Best Paper Award at VMV 2019

1 min read ·
Fourth-year computer science Ph.D. student Jinhui Xiong won the best paper award at this year’s 24th International Symposium on Vision, Modeling, and Visualization (VMV) 2019.

Yidan Zheng

Ph.D. Student, Computer Science
Yidan is a MS/PhD student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Education and Early Career Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020. Research Interest Yidan is interested in developing novel computational imaging systems in both hardware and software.

Xinge Yang

Ph.D. Student, Computer Science
Xinge Yang is a computer science PhD student at KAUST, working on computational optics and computational imaging.

Xiong Dun

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiong Dun was a Postdoctoral Research Fellow in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Dun obtained his bachelor degree in Electronic Science and Technology from Harbin Institute of Technology in Harbin, China in 2007. After that in 2010, he received his master degree in Optical Engineering from Tianjin Jinhang Institute of Technology and Physics in Tianjin, China. He has a doctoral in Optical Engineering from Beijing Institute of Technology in Beijing

Simeng Qiu

Simeng Qiu is an Electrical Engineering Ph.D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career ​Simeng Qiu obtained her first bachelor degree in Communication Engineering from Harbin Institute of Technology in China. She also has a bachelor degree in Electronic and Electrical Engineering from University of Birmingham obtained in 2016. After that, she joined KAUST to continue her studies in the same field

Qiang Fu

Research Scientist, Computer Science
Qiang Fu is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qiang Fu obtained his bachelor degree in Manufacturing and Design Automation of Machinery from University of Science and Technology of China in 2007. He has a Ph. D. in Optical Engineering from University of Chinese Academy of Sciences (2012). In 2012, Qiang joined the Academy of Opto-Electronics at Chinese Academy of Sciences as an Assistant Research Fellow. After 2 years

Miao Qi

Miao Qi is an Electrical Engineering Ph.D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Miao Qi obtained his bachelor degree in Electrical Engineering from Southeast Jiaotong University in China. Later on, he received his master degree in Electronic and Electrical Engineering from Peking University in 2018. Research Interest Miao is focusing on computational photography and computational imaging

Julio Rey Ramirez

Ph.D. Student, Computer Science
Julio Rey Ramirez is a Master student under the supervision of Prof. Wolfgang Heidrich at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Julio Rey Ramirez was an electronics engineer from National University of Colombia (UNAL). He worked as laboratory assistant in the lighting testing lab (LABE) at UNAL from 2015 to 2018. Julio Rey Ramirez joined KAUST as a visiting student in 2019, and worked in the programming of machine vision cameras and computational microscopy under the supervision of professor Wolfgang Heidrich

Jinhui Xiong

Ph.D. Student, Computer Science
Jinhui Xiong is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jinhui Xiong obtained his bachelor degree in Electronic Engineering from City University of Hong Kong (CityU) in Hong Kong in 2015. Then, he joined KAUST and received his masters in Computer Science in 2017. During his bachelor study, he spent two years on CUDA programming for various projects in Dr. Jun Fan’s lab. In 2014, he worked as an assistant researcher at EMC

Guangming Zang

Research Engineer, Computer Science
Guangming Zang is a research engineer in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Guangming obtained his bachelor degree in Software Engineering from China University of Geosciences in Wuhan, China in 2013. He received his master degree in Computer Science from University of Chinese Academy of Sciences in China. Before joining KAUST, Guangming was a research assistant (2013 – 2014) at The National Space Science Center at Chinese Academy of Sciences

Hadi Amata

Hadi Amata is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Amata received his master degree in Electronics/Microelectronics from Provence University in France in 2008. He obtained a doctoral in Electrical Engineering “Optics and Photonics” from Jean Monnet University of Saint Etienne in France in 2012. He started his career life as a teaching assistant at Saint-Étienne Institute of Technology (IUT) in 2010. He was also a teaching

Congli Wang

Congli Wang is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Congli Wang obtained his bachelor of engineering in Electrical Engineering from Tianjin University in China in 2015. After that, he joined KAUST and received his master degree in Electrical Engineering in 2016. Research Interest Congli is focusing in his research on Wavefront Sensing, Adaptive Optics, Computational Imaging and Computational Photography. He is working on

Abdulrahman Alanazi

Visiting Scholar (former), Information Science Lab
Visiting Scholar, Information Science Lab at Computer, Electrical and Mathematical Science and Engineering (CEMSE) , Assistant Professor at King Saud University
<> healthcare (16)

Babar Khan

Ph.D. (former), Bioscience
Ph.D. degree in King Abdullah University of Science and Technology (KAUST)

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Radio Frequency Sensing in Healthcare: Detection of Falls, Gait Abnormality and Vital Signs

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B9 L2 H1

In this talk, we represent recent advances of radio frequency (RF) sensing technology for healthcare, specifically in monitoring human activities inside homes, retirement facilities, and hospitals. Sensing technologies and data analytics are considered powerful tools in efficient indoor monitoring of human activities. Monitoring of activities of daily living (ADL) can identify falls, which are considered as the leading cause of fatal and non-fatal injuries for people aged 65 and over. It can also detect variants in activity patterns and changes in routines and lifestyle as well as the state of physical, cognitive, and psychological health of the person. In addition to monitoring ADL, RF-based gesture recognition using hands and arms is shown to be an important contactless technology for Man-Machine-Interface (MMI). Adding to the indoor applications, RF-based vital sign monitoring has vast medical use, as respiration and heartbeats are essential diagnostic barometers for many health problems. More recently, RF sensors have also been proposed for gait analysis for rehabilitation and timely diagnosis of many neurological, orthopedic and medical conditions. Changes in gait patterns can also be precursors of falls. In this talk, we present successful examples in each of the above application areas and discuss pertinent open problems worthy of investigations.

Exploring the Potentials of EM Waves from Body-scale to Nano-communications for Healthcare and Next Generation Wireless Applications

Akram Alomainy, Reader, Antennas and Applied Electromagnetics, Queen Mary University of London

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KAUST

With the advent of commercial products, such as Apple iWatch and Samsung Galaxy Gear, body-centric communication has increasingly garnered the public attention and smoothly translated state-of-the-art research work into reality. However, challenges still remains and these are often fundamental physical hurdles that need to be further explored and investigated to come up with efficient and scalable solutions applicable to many fields and areas. This becomes an important research area when you look at the scale or rather the multiple scales it needs to work at from body-size or larger networks to the nano-scale where there have been lots of interest recently on how to get nano-devices inside tissues and even inside intelligent materials around us. The talk will present recent development in the area of antennas, RF devices and electromagnetic solutions for applications such as healthcare, biomedical engineering and next generation wireless communications. It will look at the challenges from theoretical, numerical and experimental prospective to ensure that proposed concepts and outcomes are of benefit not only to those domains but other beyond such as agricultural technology and smart home and cities.

Smart healthcare uses stats to spot a stumble

1 min read ·
Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Zooming in on brain function

1 min read ·
Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Today we celebrate World Statistics Day!

1 min read ·
Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

First joint publication between KAUST and MBSC seeks to improve treatment options for chronic kidney disease in Saudi Arabia

1 min read ·
Chronic kidney disease (CKD) is among the most common causes of death in Saudi Arabia. According to the 2018 edition of the Institute for Health Metrics and Evaluation’s annual Financing Global Health report, the prevalence of CKD among the Kingdom’s general population stands at 5.44% and accounts for a 4.04% mortality rate.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Sahika Inal

Associate Professor, Bioengineering
Professor Sahika Inal's research bridges the fields of organic electronics and biology, focusing on the development of bioelectronic materials for real-time health monitoring and intervention.

Adeeb Noor

Visiting Professor, Bio-Ontology Research Group
Adeeb Noor is an Associate Professor of Computer Science at King Abdulaziz University (KAU), where he is the founder of the King Abdulaziz University Hospital patients portal (SHIFAA). He is also a visiting professor and part of the Bio-Ontology Research Group in KAUST. Research Interests Dr. Noor areas of research include but are not limited to data and business governance, healthcare digital transformation, drug-drug interactions, and personalized medicine. Selected Publications​ Agent-enabled task offloading in UAV-aided mobile edge computing, R Wang, Y Cao, A Noor, TA Alamoudi, R Nour -
<> image processing (16)

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

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B1 B4 B4214

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Tarig Ballal Khidir Ahmed

Research Scientist Laboratory for Nanomedicine, Computational Bioscience Research Center King Abdullah University of Science and Technology | KAUST

Shahzad Gishkori

Postdoctoral Research Fellow (former), Information Science Lab
Postdoctoral Research Fellow, Electrical and Computer Engineering , King Abdullah University of Science and Technology.

Sara Rojas Martinez

Sara Rojas Martinez is a Ph.D. student in the KAUST Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, Sara obtained a master’s degree in Biomedical Engineering from Universidad de Los Andes, Bogotá, Colombia. Research Interests Sara is interested in topics related to the investigation in Artificial Intelligence and Computer Vision. . Education Profile • M.S., Biomedical Engineering, University of Los Andes, Colombia, 2018 • B.E., Artificial Intelligence in Electronics Engineering, University of Los Andes, Colombia, 2017

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzi Idoughi

Research Scientist, Computer Science
Ramzi Idoughi is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ramzi obtained his engineering degree in Space System and Image Processing from National Higher French Institute of Aeronautics and Space (ISAE/SUPAERO) in France in 2011. Later, he received his master degree in Signal, Image, Acoustics and Optimization (SIAO) from Higher Educational Institution in Toulouse (ENSEEIHT) in France in 2011. After that in 2015, he received his doctoral

Peihao Li

Peihao Li is a Ph.D student in Electrical Engineering department within CEMSE division. He got a B.S in Observation, Navigation and Control from Northwestern Polytechnical University, China. He received master degree in the Ms/PhD program in Electrical Engineering at King Abdullah University of Science and Technology (KAUST) in winter 2017. His research interests include UAV navigating, image processing and robust control. Currently, he is working on developing algorithms and applications in signal processing and is especially interested in semi-classical signal analysis (SCSA). Research

Mohammed Ali Abugurain

Visiting student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Islam Almasri

Islam Almasri obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Islam is a Ph.D. candidate in the Computer Science program under the supervision of Prof. Nina Fedoroff. at Fedoroff Research Group. Islam is specialized in developing various software tools for solving problems in other scientific domains, including Image Processing, Medical Imaging, Chemical Networks, and Protein Structure. Selected Publications Almasri, Islam

Heba Almorad

Visiting student in the Communication Theory Lab (CTL) and the Information System Lab (ISL), at King Abdullah University of Science and Technology,

Alejandro Pardo

Alejandro Pardo is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) program under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Alejandro has an Engineer’s degree in Artificial Intelligence in both Electronics and Biomedical Engineering from the University of Los Andes in Colombia in 2017. He continued his studies at the University of Los Andes and received his Master’s Degree in Biomedical Engineering in 2018. He visited KAUST as a

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is
<> Control Theory (15)

Control and Estimation Designs Using Model-Based and Model-Free Approaches for Water Quality Monitoring in Process Systems

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B1 L4 R4214

This dissertation develops and evaluates advanced control and estimation strategies to address complex dynamics and measurement limitations in water-related applications, specifically optimizing fish growth in aquaculture and estimating bacterial concentration in wastewater treatment plants.

Fahad Aljehani

Designing advanced control algorithms and estimation techniques to optimize water quality systems, specifically aquaculture and wastewater treatment, blending control theory with cutting-edge AI and machine learning to enhance performance, sustainability, and cost-efficiency.

Yasmine Marani

Yasmine is a Ph.D. candidate in Electrical and Computer Engineering under the supervision of Prof. Eric Feron and Prof. Meriem Laleg. She specializes in control theory, robotics, and artificial intelligence. Her research focuses on estimation problems for complex nonlinear systems with application to unmanned aerial vehicles (UAV) in uncertain environments.

Yi-Hsuan Chen

Education 2020.08 - 2022.04 M.S. Student in Mechanical Engineering, KAUST, KSA " Control design and analysis for reduced-gravity atmospheric flights." 2015.09 - 2019.06 B.S. in Aeronautics and Astronautics, National Cheng Kung University, Taiwan Professional Profile 2019.09 - 2020.06 Teaching Assistant, Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan Honors and Awards ​2019.06 Honorary Member of Phi Tau Phi Scholastic Honor Society 2018.05 Professor Li Ke-Rang Scholarship Selected Publications Lien, Y.-H.; Peng, C.-C.; Chen, Y.-H. "Adaptive Observer-Based

Wenqi Cai

Wenqi Cai is an Ms/Ph.D. student in the Electrical Engineering department within CEMSE division. She got a B.Sc from Northeastern University and joined King Abdullah University of Science and Technology (KAUST) in Fall 2018. Currently, She is working on the Laser beam pointing control for Optical communication systems. Research Interests Wenqi's research interests include Underwater optical communications, Visible light communication, and Optoelectronics. Optical communication nowadays is a hot topic; it mainly includes two aspects: free space optical communication (FSO) and underwater optical

Samet Guler

Postdoctoral Research Fellow, Electrical and Computer Engineering
Samet Güler (M’16) received the B.S. degree in electrical and electronics engineering from Eskisehir Osmangazi University, Turkey, in 2010, the M.S. degree in electrical and electronics engineering from Bilkent University, Turkey, in 2012, and the Ph.D. degree in mechanical and mechatronics engineering from University of Waterloo, Waterloo, ON, Canada, in 2015. He worked as a short term Researcher at the University of Waterloo in 2016. He is currently a Postdoctoral Fellow at the Robotics, Intelligent Systems and Control Lab in King Abdullah University of Science and Technology, Kingdom of

Ryan Maxwell

Mohammad Aljohany

Education 2021 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA M.S. in Electrical and Computer Engineering , KAUST, KSA B.S. in Instrumentation and Control Engineering, Yanbu Industrial College, KSA Areas of Expertise and Research Interests Control Theory Intelligent Systems Automation Reinforcement Learning

Mohammad Alharbi

Mohammad Alharbi is a master student in Electrical Engineering department within CEMSE division at King Abdullah University of science and Technology (KAUST). He received his bachelor's degree in Electrical Engineering from King Abdul Aziz University in Saudi Arabia and joined KAUST in Fall 2017. Currently, he is working on experimental implementation of robust control strategies for parabolic solar collectors. Research activities Nowadays, the decrease in fossil resources along with the increase of their environmental impact is driving the world to renewable energy to meet the energy demand

Mohamad T. Shahab

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education 2020, PhD in Electrical and Computer Engineering, University of Waterloo 2015, MSc in Systems Engineering, KFUPM 2007, BSc in Control & Instrumentation Systems Engineering, KFUPM Professional Profile 2020 - Present, Postdoctoral Fellow, RISC Lab, KAUST 2016 - 2020, Research/Teaching Assistant, University of Waterloo 2012 - 2015, Research Engineer, Baker Hughes 2007 - 2011, Research/Teaching Assistant, KFUPM Selected Publications O. Wali, M.T. Shahab, E. Feron, "A Non-planar Assembly of Modular Tetrahedral-shaped Aerial Robots", 2023 IEEE International Conference on Robotics and

Lilia Ghaffour

Research Interests Lilia Ghaffour is a Ph.D. candidate in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at KAUST. She has a master degree in Applied Mathematics from the University of Mostaganem (Mathematics and Computer Science), Algeria. She joined the Ph.D. degree program in Appl. Math. and Computational Sc. at King Abdullah University of Science and Technology (KAUST) in fall 2016. She is interested in the study of control and inverse problem for fractional partial differential equations. Education Profile August 2016: PhD candidate in the Division

Fatema Alhani

Education 2020.08 - Present M.S. D. Student in Electrical Engineering, King Abdullah University of Science and Technology, KSA 2016.07 - 2020.05 B.S. in Electrical and Computer Engineering, North Carolina State University, USA

Chadia Zayane

Postdoctoral Research Fellow, Electrical and Computer Engineering
Areas of Expertise and Research Interests Control Theory Education Profile PhD in Electrical Engineering from Mines ParisTech, Paris, France (2011) M.SC in Applied Mathematics from Ecole Normale Superieure (ENS Cachan), Paris, France (2007) B.SC in Electrical Engineering from SUPELEC, Paris, France (2005)

Asem Al Alwan

Asem Alalwan is a Ph.D. candidate in the Estimation, Modeling, and Analysis group (EMAN) supervised by Prof. Meriem Laleg at King Abdullah University of Science and Technology (KAUST).​ Asem has graduated with a bachelor degree in Electrical Engineering from King Fahad University of Petroleum and Minerals KFUPM) in 2014. He joined KAUST in 2014 and earned his Master degree in 2016.​ Research Interests Asem's research is focused in the area of control and design of Free-space optical (FSO) communication systems. FSO has emerged as a viable technology that offers a large capacity usage (data

Ania Adil

Postdoctoral Research Fellow, Electrical and Computer Engineering
Ania Adil is a Postdoctoral fellow at the KAUST Robotics, Intelligent Systems, and Control Lab (KAUST RISC Lab) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Eric Feron.
<> COVID-19 (15)

Ultraviolet semiconductor technologies for COVID-19 and beyond

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KAUST

COVID-19 is a wake-up call for public health and safety. Since the pandemic began, the ultraviolet (UV) technologies have been widely employed in numerous places to contain and eliminate the viruses. Although UV light is nonchemical and does not contaminate the environment, however, most of the UV light today comes from the toxic mercury lamps. In our lab and many institutions in the world, researchers are developing semiconductor-based UV technologies that are compact, reliable, and nontoxic. This seminar shows the research background and briefly discusses the research progress made by our team in a few key areas including material growth, physics, and device fabrication for UV lasers, LEDs, and photodetectors.

SIAM CSE21 (VIRTUAL) - Minisymposium on HPC Response to the COVID19 Pandemic

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KAUST

Abstract This minisymposium brings together experts in numerical simulation that have developed HPC software tools toward a better understanding coronavirus and the global COVID19 pandemic. Their recent results highlight the importance of computational science as a guiding tool that helps in uncovering the structure and mechanics of the viral spread as well as drastically reduces the number of candidate treatments that need laborious laboratory testing. From transformative medical advances to drug discovery driven by massively parallel supercomputing, this minisymposium will span a range of

A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

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KAUST

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

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B2 L5 R5220

During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

Membrane makeover makes face mask reusable

1 min read ·
A polyimide membrane with regularly sized and spaced pores turns the N95 respirator into a reusable mask for protection against COVID-19.

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

Making the invisible, visible

1 min read ·
As bioscience and medicine progress, we are now able to live with diseases, which were once a death sentence. And we are rapidly discovering new interactions at the microscopic level that give us insight into the engines of life. However, as research gets more complicated, the layperson gets left further and further behind. Even those who trust the work of scientists can feel overwhelmed when attempting to read the latest research.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Professor Hussain’s adaptive tech for distance learning project receives $20,000 in prize funding

1 min read ·
Muhammad Hussain, KAUST professor of electrical engineering and PI of the MMH Labs, was recently chosen as one of six finalists in the KAUST Community COVID-19 Innovation Challenge. The event, organized by KAUST Innovation and Economic Development, sought to generate community-driven solutions to challenges faced during the ongoing COVID-19 pandemic.

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.

KAUST startup tackles COVID using AI and robotics

1 min read ·
The COVID-19 pandemic has activated vital innovation and R&D initiatives to tackle the crippling effects, health ramifications, and overall impact of the coronavirus on global societies and economies. In recent months, the King Abdulaziz City for Science and Technology (KACST), based in Riyadh, Saudi Arabia, launched a COVID-19-themed virtual accelerator aimed at supporting talented innovators, aspiring entrepreneurs, and innovative small companies dedicated to finding solutions to mitigate the spread of the virus through protection, prevention, tracking/detection, and treatment solutions.

KAUST scientists develop virus mutation tracker

1 min read ·
​KAUST scientists have developed a user-friendly COVID-19 mutation tracking system, useful for authorities and scientists to quickly detect variants in their region, allowing them to make speedy policy and public health decisions.

Coronavirus testing made quick and easy

1 min read ·
New diagnostic technique could help contain the spread of COVID-19 and other viral outbreaks.
<> earth science and engineering (15)

Securing future energy supply

1 min read ·
KAUST’s Earth Science and Engineering Program is underpinned by a drive to develop sustainable ways to get the best from natural resources and quantify environmental hazards.

Reconstructing the Red Sea's climate patterns

1 min read ·
An advanced numerical model is helping researchers better understand the variability of the Red Sea’s climate patterns.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Winds and waves winding down

1 min read ·
Wind intensities and wave heights in the Red Sea are decreasing over time—potential for a significant impact on its ecosystem.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Simulating Red Sea water exchanges

1 min read ·
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Georgiy L. Stenchikov

Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.
<> HPC (15)
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.
KAUST-CEMSE-The-International-Research-Conference-on-Multi-Grid-and-Multi-Scale-Methods-in-Computational-Science

KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

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Building 19 and 20

The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.

SIAM CSE21 (VIRTUAL) - Minisymposium on HPC Response to the COVID19 Pandemic

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KAUST

Abstract This minisymposium brings together experts in numerical simulation that have developed HPC software tools toward a better understanding coronavirus and the global COVID19 pandemic. Their recent results highlight the importance of computational science as a guiding tool that helps in uncovering the structure and mechanics of the viral spread as well as drastically reduces the number of candidate treatments that need laborious laboratory testing. From transformative medical advances to drug discovery driven by massively parallel supercomputing, this minisymposium will span a range of

ISC21 Workshop: Numerical Algorithms and Libraries for Exascale (NAL-X)

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ISC21 KAUST Frankfurt Germany Time CET

Abstract With the hardware technology scaling and the trend on heterogeneous chip design, the existing numerical algorithms and software framework may break down due to load imbalance. There is currently a fundamental mismatch between the underlying hardware architecture with high thread concurrency and the software deployment of numerical libraries, which relies on the traditional bulk synchronous programming model. Numerical software should first squeeze performance out of single node by efficiently running on manycore architectures with processor counts sharing a common memory in the

Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications (SC21 Panel)

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SC21 Saint Louis MO USA

Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications SC21 Panel Time: Tuesday, 16 November 2021, 10:30am - 12pm CST Location: 225-226 (America's Center, St. Louis, MO USA) Abstract With the end of Moore’s Law, the community has witnessed new hardware trends to increase performance. Today, it is not only the traditional x86 and accelerators that are part of computing systems, but also ARM, FPGAs and dedicated processors for DL workloads that equip now pioneering HPC systems. By the end of this decade, we are moving towards an era of extreme scale with “extreme

PhD Defense of Tariq AlTurkestani

1 min read ·
Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems PhD Dissertation Defense by Tariq Alturkestani PhD Student, Computer Science Thursday, July 9, 16:00 - 17:00, https://kaust.zoom.us/j/94054511362 Contact Person: Tariq Alturkestani The execution rate of floating-point operations has typically increased by an order of magnitude every four years during the last 30 years of modern comput ing. This exponential growth in terms of computational power has benefited from the hardware technology scaling. However, memory and storage systems have not maintained

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

ECRC contributions to SIAM CSE21

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ECRC @ SIAM CSE21! Follow us @KAUST_ECRC As in previous SIAM conferences on Parallel Processing (PP) and Computational Science and Engineering (CSE), ECRC members and their collaborators will have a strong presence at SIAM CSE21, nominally in Fort Worth, Texas, but held virtually due to the pandemic. ************************************************** SIAM CSE21 will run virtually with live sessions. ************************************************** Registration to SIAM CSE is required to gain access to the online platform and attend live sessions. Once registered, please use your personal

KAUST presents exascale expertise at SC21

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Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non

Steering Customized AI Architectures for HPC Scientific Workloads: A Bird of Feather at SC22!

1 min read ·
Abstract: We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems. We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid

Mixed Feelings About Mixed Precisions: Birds of a Feather at SC22!

1 min read ·
Abstract: Come and share your experiences with the state-of-the-art of mixed-precision techniques! By wisely trading off accuracy, we can mitigate data movement overheads and increase performance of applications, including real-time adaptive optics simulations on ground-based telescopes and genome-wide association study for agricultural genomics. No free lunch, however: these optimizations require support from the software/hardware ecosystem and strong numerical validation. For instance, the tile Cholesky factorization is a fundamental matrix algorithm in solving covariance problems that

Mixed Feelings About Mixed Precisions: Birds of a Feather at SC23!

1 min read ·
Abstract What if we have been oversolving in computational science and engineering for decades? Are low precision arithmetic formats only for AI workloads? How can HPC applications exploit mixed-precision hardware features? This BoF invites the HPC community at large interested in applying mixed precisions into their workflows and discussing the impact on time-to-solution, memory footprint, data motion, and energy consumption. Experts from scientific applications/software libraries/hardware architectures will briefly provide the context on this trendy topic, share their own perspectives, and

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Esmail Abdul Fattah

Ph.D., Statistics

HPC

Introduction I am currently serving as a Postdoctoral Researcher at the Extreme Computing Research Center at KAUST, where I have the privilege of working under the leadership of the center's director, Professor David Keyes. My research journey here is further enriched by the close collaboration and guidance I receive from Dr. Hatem Ltaief, whose expertise significantly contributes to my professional growth and research endeavors in this dynamic field. My academic journey led me to earn a Ph.D. in Statistics under the mentorship of Professor Håvard Rue, working within his esteemed research
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Control and Estimation Designs Using Model-Based and Model-Free Approaches for Water Quality Monitoring in Process Systems

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B1 L4 R4214

This dissertation develops and evaluates advanced control and estimation strategies to address complex dynamics and measurement limitations in water-related applications, specifically optimizing fish growth in aquaculture and estimating bacterial concentration in wastewater treatment plants.

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering
Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student

Fahad Aljehani

Designing advanced control algorithms and estimation techniques to optimize water quality systems, specifically aquaculture and wastewater treatment, blending control theory with cutting-edge AI and machine learning to enhance performance, sustainability, and cost-efficiency.

We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.

Thoughts About Machine Learning - Week 7

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B9 LH1 R2322

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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B9 H1 R 2322

The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Rihan Huang

M.S. Student, Computer Science

Mohammad Aljohany

Education 2021 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA M.S. in Electrical and Computer Engineering , KAUST, KSA B.S. in Instrumentation and Control Engineering, Yanbu Industrial College, KSA Areas of Expertise and Research Interests Control Theory Intelligent Systems Automation Reinforcement Learning

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Fares Fourati has studied mathematics and physics at the University of Sfax and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2017. In 2020, he received his diplôme d’ingénieur with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. In 2022, he received his Master of Science in electrical and computer engineering from KAUST. Early Career Before

Deyao Zhu

Ph.D. Student, Computer Science
Deyao Zhu is a Computer Science Ph.D. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Early Career Deyao obtained his Master's degree in Electrical Engineering and Information Technology at Gottfried Wilhelm Leibniz Universität Hannover, Germany. His Master thesis is done in Max Planck Institute for Intelligent Systems which focuses on representation learning. Before that, he received his Bachelor’s degree in Mechatronics at Tongji University

Chang Shu

Education 2021.08 - Present M.S. in Electrical Engineering, KAUST, KSA 2017.09 - 2021.06 B.Eng in Electrical Engineering, UESTC, China 2017.09 - 2021.06 B.Eng in Electrical Engineering, University of Glasgow, UK Honors and Awards ​2020.11 China National Scholarship 2019.04 Meritorious Winner in the 2019 Interdisciplinary Contest in Modeling (ICM) Areas of Expertise and Research Interests Reinforcement Learning Robotic Assembly Manipulation Planning

Aniq Ur Rahman

Aniq is an MS/PhD student under the supervision of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL), KAUST. Education and Early Career Aniq obtained his bachelor’s degree in Electronics and Communication Engineering from National Institute of Technology Durgapur, India in 2019. Upon graduation, he joined Qualcomm as an Associate Systems Engineer. In 2018 summers, he participated in the ALICE experiment as a research intern at the European Organization for Nuclear Research (CERN), Switzerland. Prior to joining KAUST, he worked as a Project Assistant at IISc 5G Testbed, Indian
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iHunter: Empowering Autonomous UAVs for Robust, Real-Time Aerial Terget Tracking and Trajectory Prediction

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B9 L2 R2325

This talk introduces iHunter, an integrated framework for UAVs to reliably detect, track, and predict the trajectories of moving targets by fusing multi-modal perception with state estimation, employing kinematic predictors and a GRU-based neural network trained on synthetic data, and demonstrating improved tracking stability and predictive performance for aerial surveillance and target interception.

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Data assimilation and optimal control in the context of UAV-based flash flood monitoring

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B5 L5 R5209

Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. The emergence of Unmanned Aerial Vehicles (UAVs) gives the possibility to monitor these events over large geographical areas. In this talk, we focus on the problem of trajectory planning for a swarm of unmanned aerial vehicles sensing flooding conditions.

Civil drones traffic management: Wireless communication for safe UAV

Prof. Sofie Pollin, KU Leuven

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KAUST

UAV

In this webinar, we describe the common terminology for these two communities. First, the traffic management system architecture, requirements, terminology, and services are discussed. A quick overview of existing technologies that can be useful for aerial deconfliction. Next, the focus will shift to wireless technologies used for the tactical (while flying) deconfliction: ADS-B, p2p LoRa, WiFi, FLARM.

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Robots learn by checking in on team members

1 min read ·
Innovative drone designs and software enables a team of drones to work together in a coordinated approach.

Teaching algorithms to see

1 min read ·
Four hundred hours of video are uploaded to YouTube every minute. Other than using text to search the title, description or tags associated with a video, video content search is limited. In addition to helping users find content more quickly and accurately, the ability to search video content is of paramount importance to video platforms and advertisers. Last year, YouTube was embroiled in controversy after rolling video ads for Coca-Cola and Amazon, among others, were shown before racist and extremist content. Advertising content is a major revenue stream for video platforms like YouTube

KAUST Ph.D. student Matthias Mueller wins award for drone research

1 min read ·

UAV

KAUST Ph.D. student Matthias Mueller, who works in the Image and Video Understanding Lab under the supervision of KAUST Associate Professor Bernard Ghanem, won the Best Paper/Presentation Award at the 2nd International Workshop on Computer Vision for Unmanned Aerial Vehicles (UAVs). UAVs are commonly known as drones. The workshop was held in September 2018 in Germany in the post-proceedings of the 15th European Conference on Computer Vision workshops. Müller's paper entitled "Teaching UAVs to Race: End to End Regression of Agile Controls in Simulation" outlines the research of Müller and his

ISL PhD Student Osama Bushnaq does Internship at UBC

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UAV IoT

ISL PhD Student Osama Bushnaq did an internship at the University of British Coloumbia in Canada. His internship was focused on UAV-IoT. Every year, an average of 2.5 million hectares (ha) is burnt only in Canadian forests, which costs around 500 million to 1 billion Canadian dollars per year. The current forest fire detection methods such as satellite imaging and infrared cameras are not reliable. In order to detect forest fires before getting out of control, a new fire detection method based on Unmanned Aerial Vehicle Internet of Things (UAV-IoT) network was studied during this internship. A

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Mohammed Aldosari

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2018.08 - 2020.06 M.S. in Mechanical Engineering, KAUST, KSA 2011.08 - 2016.05 B.Sc. in Aerospace Engineering, KFUPM, KSA Professional Profile 2022.01 - 2022.05 Teaching Assistant, CEMSE, KAUST, KSA 2017.07 - Present Researcher, Center of Excellence for Aeronautics & Astronautics, King Abdulaziz City for Science and Technology, KSA 2017.04 - 2017.07 Maintenance Planning Engineer, SaudiGulf Airlines, KSA Selected Publications M.N.Aldosari, K.K.Yalamanchi, X. Gao, S.M. Sarathy, "Predicting entropy and heat capacity

Kuat Telegenov

An experienced Robotics Research Engineer and Laboratory Manager.

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.
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Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

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B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

6G: Super-connecting the connected and connecting the unconnected

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KAUST

6G

This talk aims to 1) provide an envisioned picture of 6G, 2) serve as a research guideline in the beyond 5G era, and 3) go over the recently proposed solutions to provide high-speed connectivity in under-covered areas to serve and contribute to the development of far-flung regions. The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. It is indeed a well-known fact that the transmission of the information at a rapid pace has transformed all spheres of human life such as economy, education, and health to name a few. In this context, and as the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, and 5G networks are in their early stage of deployment, the research visioning and planning of the sixth generation (6G) of WCSs are being initiated. 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s.

6G: A new frontier for the design of NOMA

Prof. Zhiguo Ding, University of Manchester

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KAUST

6G

The aim of this talk is to provide an overview of the latest research results and innovations in NOMA technologies as well as their emerging applications. Future research challenges regarding NOMA in B5G and 6G are also presented.

An Overview of 6G Requirements and Potential Enabling Technologies – A Mobile Network Operator's Perspective

Dr. Youssouf Ould Cheikh Mouhamedou, Technology Alliances and Partnership Supervisor, Saudi Telecom Company (STC)

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B1 L3 R3119

The success of 5G from business perspective (creation of revenue and growth) will have a big say on the future shape of 6G. In fact, 5G standard is still evolving with new 3GPP releases issued periodically, leading to 5G-Advanced and possibly 5G-Advanced Pro in coming years. Also, 5G ecosystem is still at a relatively early stage of development. However, all major players (researchers, vendors, mobile network operators, regulators) have already started their 6G activities in line with the tradition of having G+ every 10 years. This talk is about 6G, taking into consideration mobile network operator's point of view in terms of practical and economic issues. It will provide an overview of mobile networks evolution, from 1G to 6G. It will also discuss the gap between 5G vision and 5G reality and the reasons behind it. Further, 6G key requirements as well as a tentative roadmap and timeline will be presented. Finally, some potential enabling technologies will be discussed.

Bruce Lee Inspired Fluid Antenna Systems for 6G

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B1 L3 R3119

In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some early results on fluid antenna systems, which shows its potential for improving wireless communication performance.

6G Summit on Connecting the Unconnected

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B19 L3 H1

The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. As the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s. With this background, this Summit aims to go over the recently proposed solutions not only to connect the unconnected/under-connected but also to super-connect the connected.

Plugging into a 6G future with users at the center

1 min read ·
For a communications revolution, 6G development needs more human-centric research.

6G: The time is now!

1 min read ·
While 5G companies and opponents are competing over the deployment of the first 5G platforms, the next generation of wireless telecommunication technology is being concocted right now in laboratories all around the world.

New research aims to bridge the digital divide

1 min read ·
By the end of 2018, there were nearly 4 billion people without Internet connectivity in the world. Almost 75% of this unconnected population is located in 20 countries and is concentrated in rural, low-income, and low-literacy areas.

Connecting the Unconnected at the KAUST 6G Summit

1 min read ·
The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

Developing the future of digital inclusivity

1 min read ·
The second edition of the KAUST 6G Summit on "Connecting the Unconnected" was recently held online from Tuesday, September 28 to Thursday, September 30, 2021. As part of “The Decade of Digital Inclusion,” a semester-long master class from the Marconi Society, the summit covered the latest research and technological advancements in 6G global connectivity.

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.

Lin Chen

Lin is a visiting student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST) Education and Early Career Lin is a 3rd-year bachelor student in Communication Engineering from University of Electronic Science and Technology of China (UESTC). Research Interest Lin is interested in enhancing the performance of the communication system to meet the communication requirements of the evolving new application scenarios. She’s currently exploring the application of RIS and artificial intelligence in
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IEEE Antennas and Propagation Society Distinguished Lecturer Workshop

Call for Registration: IEEE Antennas and Propagation Society Distinguished Lecturer Workshop

Join us from February 2–4, 2025, for the IEEE Antennas and Propagation Society Distinguished Lecturer Workshop at KAUST Auditorium between Buildings 2 and 3.

An antenna that goes the distance

1 min read ·
A low-cost, copper-based flexible and stretchable antenna improves far-field data communication in wearable electronics.

Two KAUST students selected for the Three Minute Thesis 3MT competition final at IMS2017

1 min read ·
This year, KAUST took the lion's share at the Three Minute Thesis (3MT(R)) contest at the annual IEEE microwave symposium IMS2017, with two students selected to take part in the final and one of them - Dr. Farhan Abdul Ghaffar - finishing within the top 4 and getting Honorable Mention for his presentation.

Tuning in to magnetic ink

1 min read ·
A new ink containing iron oxide nanoparticles can be turned into fully printed and versatile components for cellular networks.

Meet KAUST Prospective Student: Zere Iman

1 min read ·
Zere Iman is a 22-year-old graduate who will join KAUST from Nazarbayev University, Kazakhstan. Iman will join us in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Integrated Microwaves Packaging Antennas and Circuits Technology Research Group, under the supervision of Professor Atif Shamim.

Meet KAUST prospective student: Yiming Yang

1 min read ·
Yiming Yang is an electronic science and technology graduate who will join KAUST from the University of Electronic Science and Technology of China (UESTC). Yiming will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Integrated Microwaves Packaging Antennas and Circuits Technology Research Group under the supervision of Professor Atif Shamim. After completing his studies, Yiming aspires to be a productive researcher in antenna and microwave circuit design.

Professor Atif Shamim named IEEE Antennas and Propagation Society Distinguished Lecturer

1 min read ·
KAUST Associate Professor of Electrical and Computer Engineering Atif Shamim has been selected as a Distinguished Lecturer (DL) for the IEEE Antennas and Propagation Society (AP-S) from 2022 to 2024. In his new role, sponsored by the IEEE, Shamim will give lectures to AP-S chapter members on topics of interest and importance to the global AP community.

KAUST students' research wins first place at IEEE AP-S/URSI 2022

1 min read ·
Hanguang Liao and Yiming Yang from the KAUST IMPACT Lab won first place in the 2022 IEEE AP-S Student Design Contest at the 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (IEEE AP-S/URSI 2022), held in July in Denver.

Zubair Akhter

Research Interests ​Antennas: Multiband, Ultra wide band, highly directive array Microwave Imaging: Detection of concealed objects for Homeland Security Microwave Sensing and Characterization: Wireless sensing for Biomedical and Oil and Gas Industry. Concurrent Multi-band RF Front-End Design Selected Publications Zubair Akhter, Abhishek Kr. Jha and M Jaleel Akhtar, "Generalized RF Time-Domain Imaging Technique for Moving Objects on Conveyor Belts in Real Time," in IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 7, pp. 2536-2546, July 2017. [ Link] Zubair Akhter and M. J

Zere Iman

Zere Iman is an M.S./Ph.D. candidate in the KAUST Integrated Microwaves Packaging Antennas and Circuits Technology Research Group (IMPACT), under the supervision of Professor Atif Shamim. Before joining KAUST, Zere obtained a bachelor's degree in Electrical and Computer Engineering from Nazarbayev University, Kazakhstan. Research Interests Zere's research interests include antennas and electromagnetism.

Yiming Yang

Yiming Yang is a Ph.D. candidate at the KAUST Integrated Microwaves Packaging Antennas and Circuits Technology Research Group under the supervision of Professor Atif Shamim. His research interests include frequency selective surfaces, reconfigurable intelligent surfaces, and meta-surfaces in combination with additive manufacturing, time modulation, and other fabrication and reconfiguration methods.

Nidhi Mishra

Nidhi Mishra worked as a Researcher at Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Nidhi's research interests included ​Printed electronics, Antennas, and Microwave circuits. Education Profile Bachelor's in Electronics and Instrumentation, M.J.P. Rohi​lkhand University​​, India. Master's in Digital Communication, Uttarakhand Technical University, India.

Muhammad Fahad Farooqui

Muhammad Fahad Farooqui graduated with a Ph.D. degree in Electrical Engineering from KAUST under the supervision of Professor Atif Shamim. He is now a Post Doctoral Fellow in Concordia University Canada​. Muhammad has over 10 years of experience in design, development, integration, and testing of RF/microwave/millimeter-wave circuits and systems and antennas. He is also an expert in developing wireless sensor nodes using additive manufacturing such as 3D printing and inkjet printing for environmental and health applications. His research has been published in over 20 publications in peer

Atif Shamim

Professor Shamim is an accomplished electrical engineer specializing in wireless communication and sensing components and systems. His work aims to improve the performance of wireless communication and sensing systems through low-cost additive manufacturing techniques.
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KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

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KAUST

The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together.

Engineering Organic Electronics for Biology - 2019-10-13

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B9 L2 H1 R2322

The field of bioelectronics combines the worlds of electronics and biology with the aim of developing new tools for biomedical research and healthcare. The majority of implantable devices are mechanically stiff and the mechanical properties mismatch with soft tissue causes an immune response which results in their rejection from the body. Another limitation is associated with the fact that most devices utilize metal electrodes to record from/stimulate tissue. These electrodes offer limited coupling with ion fluxes used by cells to communicate with each other, resulting in low efficiency. Such challenges can be overcome with the integration of soft, conducting polymers displaying mixed (ionic and electronic) conduction. In this talk, I will present approaches that leverage the properties of organic conducting materials in order to develop bioelectronic devices interfacing with the body. These devices include organic electrochemical transistors for measuring metabolites, neural activity and integrity of cellular layers.

Engineering Organic Electronics for Biology - 2020-03-01

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B9 L2 H1 R2322

The field of bioelectronics combines the worlds of electronics and biology with the aim of developing new tools for biomedical research and healthcare. The majority of implantable devices are mechanically stiff and the mechanical properties mismatch with soft tissue causes an immune response which results in their rejection from the body. Another limitation is associated with the fact that most devices utilize metal electrodes to record from/stimulate tissue. These electrodes offer limited coupling with ion fluxes used by cells to communicate with each other, resulting in low efficiency. Such challenges can be overcome with the integration of soft, conducting polymers displaying mixed (ionic and electronic) conduction. In this talk, I will present approaches that leverage the properties of organic conducting materials in order to develop bioelectronic devices interfacing with the body. These devices include organic electrochemical transistors for measuring metabolites, neural activity and integrity of cellular layers.

Mahmoud Ouda Won The "Best Out Of The Box Solution" Prize At The 18th Microelectronics Olympiad Held In Armenia

1 min read ·
Doctoral student, Mahmoud Ouda, working under the supervision of Prof. Khaled Salama, won "Best out of the box solution" prize at the eighth microelectronics olympiad held October 15-19 in Yerevan, Armenia.

KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

1 min read ·
The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” was held in KAUST between the 25th and 27th of February. This yearly event aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together, including Chemistry, Electrical Engineering, Biology, and Material Science, who are at the top of their

Using electronics to solve common biological problems

1 min read ·
What do an electrical engineer, an organic chemist, a materials scientist and a cell biologist all have in common? They invent and improve applications at the interface of biology and electronics.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

Sahika Inal

Associate Professor, Bioengineering
Professor Sahika Inal's research bridges the fields of organic electronics and biology, focusing on the development of bioelectronic materials for real-time health monitoring and intervention.

Renad Bougis

Research Interests Wireless sensors design Biosensors and bioelectronics Education ​Bachelors of Science in Electrical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA (2016-2020)​

Raphaela Kethlim Souza Silva

Raphaela Silva is currently a Master student at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Raphaela Silva received her bachelor degree from the department of Electrical Engineering in Federal University of Rio Grande do Norte, Brazil in 2018. In the following year she joined King Abdullah University of Science and Technology to work under the supervision of Professor Khaled Salama. Previously, in 2018, Raphaela worked during 6 months as a visiting student at KAUST, under the

Mahmoud Ouda

Postdoctoral Research Fellow, Sensors
Mahmoud Ouda is currently a Postdoctoral Research Associate with the Communications and Signal Processing Group (CSP), Department of Electrical and Electronic Engineering at Imperial College London. He received his Ph.D. from King Abdullah University of Science & Technology (KAUST), Saudi Arabia in 2016, and his M.Sc. and B.Sc. (with honors) both in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, in 2011 and 2006 respectively. He was a Graduate Research Assistant with the Sensors Lab. in King Abdullah University of Science & Technology (KAUST) from 2010 to

Jokubas Surgailis

Research Interest Jokūbas is currently investigating mixed conduction in organic semiconductors. Using combined electrochemical characterization methods, he aims to advance the understanding of ion-to-electron coupling. Along with his background in Electrical Engineering, Jokūbas plans to use this research to push the frontier of interfacing the two halves of Bioelectronics. KAUST Affiliations ​Biological and Environmental 
Science & Engineering Division (BESE) Education Profile 2018 - 2024: PhD student, Organic Bioelectronics Lab, KAUST, Thuwal, Saudi Arabia 2017: Visiting student, Organic

Hamzeh Abu Qamar

MS Student, Electrical, Electronics and Communications Engineering at King Abdullah University of Science and Technology (KAUST).

Anas Saleh Mohammed

Visiting Student (former), Electrical and Computer Engineering
Visiting student at King Abdullah University of Science and Technology (KAUST).
<> climate science (14)

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Challenging statistics of weather extremes

1 min read ·
More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

Chasing spatial patterns

1 min read ·
A more accurate way of resolving spatial patterns in weather could lead to better predictions of climate change.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Statistics plot pollution to inform policy

1 min read ·
The spatial variation in different air pollution components helps identify possible targets for pollution control.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.
<> cloud computing (14)

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

Computing in the Specialization Era

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B9 L2 H2 H2

In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Zuhair Yarub Khayyat

Zuhair Yarub Khayyat obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interest Zuhair's research interests included Big Data Management, Parallel and distributed data processing, Cloud Computing and Graph Analytics. His main experience was focused on problem-solving and algorithm development for processing large data in distributed environments. He is a reliable team player with a methodological mindset with the ability to utilize

Wiem Abderrahim

Postdoctoral Research Fellow, Networking Research Lab
Wiem Abderrahim is a Postdoctoral Fellow under the supervision of Prof. Mohamed Slim Alouini and Prof. Basem Shihada at the Communication Theory Lab, King Abdullah University of Science and Technology (KAUST). Education and Early Career Wiem Abderrahim accomplished her engineering studies in telecommunications with honors at the Higher School of Communications of Tunis, Carthage University in Tunisia in 2013. In 2017, she received her Doctoral Degree in Information and Communications Technologies from the same university. Before joining KAUST, she worked as an adjunct professor at the Higher

Oussama Dhifallah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering , joined Harvard University as a PhD student.

Mohammed Ahmed

Postdoctoral Research Fellow (former), Information Science Lab
post-doctoral fellow at L'École de technologie supérieure (ETS) de Montréal (Mtl) where he started on February 2014. he works with Prof. Georges Kaddoum.

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Karim Awara

Karim Awara obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis from King Abdullah University of Science and Technology. Research Interests Karim's research interests included the Database and High-Performance Computing field. Awards and Distinctions Awarded, internationally, as one of the best 5 teams in SIGMOD 2010 contest for developing high multi-query optimization techniques. And presented the work at SIGMOD conference, June 2010 in Indianapolis, IN, USA. Rewarded as one of the top four teams in Egypt in a software Engineering competition

Faisal Nawab

Faisal Nawab graduated with an MS degree in Computer Science from King Abdullah University of Science and Technology (KAUST) in 2011. Nawab currently works as an Assistant Professor of computer science and engineering in the Department of Computer Science and Engineering at the University of California, Santa Cruz, where he works on developing‏ systems for quick and accurate data analysis of new applications in cloud computing and Big Data. Education and Early Career Nawab earned a B.S degree in Computer Science from the KFUPM (King Fahd University of Petroleum and Minerals). He then studied

Essam Mansour

Postdoctoral Research Fellow, ‌InfoCloud
Essam Mansour was a Postdoctoral Research Fellow at InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST), worked with Professor Panagiotis Kalnis. He contributed to research activities of the KAUST InfoCloud Research Group including leading a research project aiming at developing an elastic database system for processing very large strings based on the cloud infrastructure. This system is to support scientific data management, as a data-intensive application. Prior to joining KAUST, Essam was a Research Associate at the School of Information Technology

Asaad S. Alghamdi

Amedeo Sapio

Amedeo Sapio has MS and PhD degree both in Computer Engineering from Politecnico di Torino, Italy. He is a Postdoctoral Research Fellow at Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Marco Canini in his research group. Research Interests ​Amedeo Sapio's research is focused on high-speed packet processing in large scale networked computer systems, data center networks, software defined networking and innovative network services. Education Profile ​Ph.D. Politecnico di Torino, Italy, 2014-2018 M.Sc. Computer

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> computational statistics (14)

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

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B1 L4 R4102

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

Coupled Sampling Methods for Filtering

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KAUST

This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

Ángel López Oriona

Postdoctoral Research Fellow, Statistics
Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental

Ying Sun

Associate Professor, Statistics
KAUST Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

David Bolin

Associate Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Carolina D. Euan Campos

Postdoctoral Research Fellow, Statistics
Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016. She joined KAUST as a Postdoctoral Research Fellow in October 2016. Research Interest High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes, Complex Data Visualization, Computational Statistics. Awards and Distinctions Sylvia Esterby Presentation Award

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> distributed systems (14)

A blindfold approach improves machine learning privacy

1 min read ·
A query-based method for extracting knowledge from sensitive datasets without showing any underlying private data could resolve long-standing privacy concerns in machine learning.

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

Breaking the Vehicle Over-The-Air Update System

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart

Distributed Robotics

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KAUST

This talk presents an overview of challenges, state-of the-art, and applications for distributed robotic systems. In distributed robotic systems, there is a group of robots that seek to achieve a collective task. Applications include environmental monitoring, search and rescue, and programmable self-assembly. Settings can range from a small team of cooperative robots to a swarm of many interacting agents. An essential feature of such systems is that individual robots make decisions based on available local information as well as limited communications with other robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized paradigm, both measurements and decisions are distributed among multiple actors.

An Overview of Systems Research at TU Munich

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B4 L5 R5209

In this talk, Professor Pramod Bhatotia will give an overview of systems research at TU Munich. He will cover his teaching and ongoing research projects. And will conclude the talk with a brief overview of his ERC project.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

1 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Ibrahim Abdelaziz

Senior Research Scientist, IBM
Selected Publications Using Advanced Hidden Markov Models for online handwriting recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy In Proceeding of Asian Conference of Pattern Recognition (ACPR), (2011) Large Vocabulary Arabic Online Handwriting Recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy International Journal on Document Analysis and Recognition, (2014) Artificial Tutor for Arabic Handwriting Training. Sherif Abdou, Aly Fahmy, Ibrahim Hosny, Eman Mostafa In Proceedings of the Second International Conference on Arabic Language Resource, (2009) Education Profile 2013 - 2018: PhD

Elmootazbellah Elnozahy

Professor (former), Computer Science
Elmootazbellah (Mootaz) Elnozahy is a renowned computer scientist who has filed, individually and with colleagues, 58 U.S. patents. He has successfully secured research and development funding grants of ~$320M.

Asaad S. Alghamdi

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals

Amedeo Sapio

Amedeo Sapio has MS and PhD degree both in Computer Engineering from Politecnico di Torino, Italy. He is a Postdoctoral Research Fellow at Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Marco Canini in his research group. Research Interests ​Amedeo Sapio's research is focused on high-speed packet processing in large scale networked computer systems, data center networks, software defined networking and innovative network services. Education Profile ​Ph.D. Politecnico di Torino, Italy, 2014-2018 M.Sc. Computer

Ali Shoker

Research Associate Professor, Computer Science
Ali Shoker, a Research Associate Professor at KAUST, explores Byzantine fault tolerance, AI, and blockchain to create practical solutions for autonomous vehicles, fintech, and satellite systems.
<> flexible (14)

Flexible, Wearable, Disposable Wireless Communication and Sensing Systems Through Additive Manufacturing - 2020

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KAUST

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems. The ability to print electronics on unconventional mediums such as plastics, papers, and textiles has opened up a plethora of new applications. In this talk, various innovative antenna and sensor designs will be shown which have been realized through additive manufacturing. A multilayer process will be presented where dielectrics are also printed in addition to the metallic parts, thus demonstrating fully printed components. Many new functional inks and their use in tunable and reconfigurable components will be shown. In the end, many system level examples of wireless sensing applications will be shown. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll and reel-to-reel printing is not far away.

Prof. Muhammad Hussain selected as distinguished lecturer for the IEEE Electron Devices Society

1 min read ·
Prof. Muhammad Hussain has been selected as a distinguished lecturer for the IEEE Electron Devices Society (EDS). The IEEE sponsored EDS Distinguished Lecturer Program exists for the purpose of providing IEEE EDS Chapters with a list of quality lecturers.

Prof. Hussain named Fellow of Institute of Nanotechnology, UK

1 min read ·
Dr. Muhammad Mustafa Hussain an Assistant Professor of Electrical Engineering from founding batch faculty has been named as the Fellow of Institute of Nanotechnology, UK.

KAUST team achieves remarkable flexibility with Silicon

1 min read ·
KAUST's Integrated Nanotechnology Lab are working to transform cutting-edge materials to make high-performance devices, but also about portability - they will have to be flexible, stretchable and reconfigurable

Stretchable Electronics Make Thermotherapy Easier for Arthritis Patients

1 min read ·
A paper has been published to cover a new technology that makes electronic devices stretchable while retaining their electrical and thermal properties.

Sensors for a light touch

1 min read ·
Biocompatible tactile sensors based on magnetic hair-like structures enable new applications for touch sensors.

Scientists made cutting edge 'smart skin' with household items

1 min read ·
Congratulations to Prof. Muhammad Hussain whose smart skin project made out of cheap household materials such as foil and tape - was featured on the Washington Post and a few other news media.

The Institute of Physics (IoP), nominated Prof. Muhammad Mustafa Hussain for the Fellowship at the Institute of Physics

1 min read ·
Prof. Muhammad Mustafa Hussain nominated for the Fellowship at the Institute of Physics (IoP), UK.

Prof. Muhammad Hussain's article selected for "2015 Applied Physics Letters Editors' Picks

1 min read ·
Prof. Muhammad Hussain's article "Functional integrity of flexible n-channel metal-oxide-semiconductor field-effect transistors on a reversibly bi-stable platform" was selected for "2015 Applied Physics Letters Editors' Picks"- published by the American Institute of Physics - a scientific journal featuring concise, up-to-date reports on significant new findings in applied physics.

Muhammad Hussain elected Fellow of the American Physical Society (APS)

1 min read ·
The Fellowship is an international distinction assigned only to highly selected scientists for their contributions in the fields of APS, and was given to Prof Hussain for contributions to exploration, evaluation, and transition of planar and nonplanar high-k/metal gate complementary metal oxide semiconductor electronics, silicon/silicongermanium/ III-V nanotube devices, and flexible, stretchable, reconfigurable complementary metal–oxide–semiconductor electronic systems.

Self-destructible electronics is a Mission Impossible no more

1 min read ·
In today's digital age, the increasing dependency on information makes everyone potentially exposed to privacy invasion and cyber security. Annual reports on tech-safety breaches are alarming, with stolen portable devices, laptops or mobiles, along with hacked hardware skyrocketing to 50% in the United States only. Bigger the picture, global is the risk of unwilling access to private lives or strategical documents, with everyone in the eye of the most dangerous of information violation. In such cases, an immediate ability to remotely destroy the sensitive parts, if it is not possible to regain them, would save us.

Wrist sensor for health screening

1 min read ·
Give a Post-It Note paper to an Electrical Engineering team and you will see it turn onto the new alley to revolutionize modern medicine; an unprecedented wearable, budget-friendly and reliable a paper-based sensor for health screening encapsulated in a silicon wrist with a recycling high-tech design. Body temperature, sweat levels, heart rate and blood pressure can now be monitored with no need to go to hospitals nor using costly technologies, dramatically rare in underdeveloped or developing countries.

Dental braces get smart

1 min read ·
-By Francesca Serra Muhammad Hussain, associate Professor of electrical engineering at the Computer, Electrical and Mathematical Science and Engineering Division of KAUST, recently developed a new dental brace technology that can straighten teeth faster and more effectively than actual products. The orthodontic system was designed to induce faster bone regeneration and to enhance protection of the enamel. According to Hussain and his team, the technology is going to introduce a paradigm shift in orthodontic treatments "The integration of high performance inorganic mechanically conformable and

Democratizing electronics one step at a time

1 min read ·
"You would not believe how many amazingly talented people there are in the world, but they often are just not exposed to opportunities," noted Muhammad Mustafa Hussain, KAUST professor of electrical engineering and currently a visiting professor at the University of California, Berkeley. He is determined to try and change this—one step at a time.
<> flexible electronics (14)

Materials and Devices for Printed Flexible Electronics

Derya Baran, Assistant Professor, Material Science and Engineering, PSE, KAUST

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KAUST

The need for big data that the internet of things (IoT) has created in recent years has turned the focus on integrating the human body in the quest to understand it better, and in turn use such information for detection and prevention of harmful conditions. Applications in which continuous and uninterrupted operation is required, or where the use of external power sources may be challenging demands the use of self-powered autonomous systems. Organic photovoltaic devices are flexible, lightweight, and soft, capable of interacting with the human body and its mechanical demands. Their processability from solutions permits their adaptation to versatile fabrication techniques such as spin coating, roll-to-roll coating and inkjet printing, with benefits including low material usage and freedom of design. In this talk, I will present how organic photovoltaics can be utilized in printed electronics as energy harvesting devices and go through the historical progress of organic/hybrid photovoltaics as well as the main activities that are ongoing in my research lab ‘Omegalab’.

Liquid Metal-Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

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B2 B3 A0215

In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon counterparts and extend their applications.

PhD Students Invited to 65th Lindau Nobel Laureate Meeting

1 min read ·
Two CEMSE doctoral students have been invited to the 65th Lindau Nobel Laureate Meeting, which will be held on June 28-July 3.

CEMSE student gets Best Paper Award at 2DFDU2016

1 min read ·
CEMSE student Chun-Ho Lin won the Best Paper Award at the 2nd International Symposium on Devices and Application of Two-dimensional Materials (2016), held in Shanghai on June 20, 2016.

Transforming electronics

1 min read ·
KAUST Professor of Electrical Engineering Muhammad Mustafa Hussain was recently elected as an Institute of Electrical and Electronics Engineers (IEEE) Fellow, effective January 1, 2020, for his "contributions to flexible and stretchable electronic circuits," IEEE stated.

Prestigious Innovation Award adds to KAUST alumna’s impressive research output

1 min read ·
KAUST alumna Joanna Nassar recently won the Best Innovation Award in the Tech for a Better World category at the 2020 Consumer Electronics Show (CES) held in Las Vegas, U.S., from January 7-10. Nassar, a postdoctoral fellow at Stanford University, received the award for her research as part of the KAUST MMH Labs research group’s development of a wearable technology tagging prototype called Bluefin.

Fiber glow brings more go for underwater communications

1 min read ·
KAUST scientists have developed an optical-based wireless network system that has significant potential for faster underwater optical communications.

Wedyan Babatain

Wedyan Babatain is a doctoral candidate of MMH Labs in the Electrical Engineering program at KAUST. She has an interdisciplinary background obtaining her bachelor's degree in Biomedical Engineering from the University of Delaware, USA, in 2017, and her master's degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), KSA, in 2019. Her research interests include sensors, actuators, microfluidics, and flexible and soft electronics for healthcare and environmental applications. She was a finalist at the 2022 SXSW Innovation Awards and has presented her

Sandeep Surya

Postdoctoral Research Fellow, Sensors
Dr. Sandeep Surya is currently a Post-Doctoral fellow at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Sandeep G. Surya received his Ph.D. degree from IIT Bombay in 2017. He is currently a Post-Doctoral Fellow with Sensors Lab, CEMSE Division, King Abdullah University of Science and Technology. Prior to this, he was a Research Associate with the Microsystems Technology Research Unit, Fondazione Bruno Kessler, Trento, Italy. His forte is in building platforms for different sensor

Rami T. ElAfandy

Rami ElAfandy obtained a Ph.D. degree in Electrical Engineering under the supervision of Professor Boon Ooi at King Abdullah University of Science and Technology (KAUST). His work was related to studying the physical properties (optical, electrical and thermal) of flexible gallium nitride (GaN) nanomembranes and engineering these properties for biological and energy applications. He developed thermal biosensors for living cells and successfully measured and differentiated between different cancer cells based on their thermal transport properties. He also worked on increasing hydrogen photo

Chun-Ho Lin

Chun-Ho Lin obtained his Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jr-Hau He in 2018. Lin completed his master’s degree in the Department of Electrical Engineering from the National Taiwan University and began his Ph.D. studies at KAUST in 2014. Research Interest Chun-Ho Lin's research interests include Perovskites, Nanophotonics, Flexible Electronics, Paper device, and 2D Materials.

Aljawharah Alsharif

Aljawharah Alsharif is a PhD student in Electrical and Computer Engineering and a member of the Smart, Advanced Memory Devices and Applications lab (SAMA) at King Abdullah University of Science and Technology, Saudi Arabia. She is passionate about shaping the future of wearable devices, flexible and soft electronics, sensors, and actuators through 4D/3D printing.

Altynay Kaidarova

​Altynay has pursued an academic career in Electrical Engineering in different countries, such as Kazakhstan, Singapore, the United Kingdom, and Saudi Arabia. She considers herself a citizen of the world upholding scientific achievements, passion, inspiration, diversity, integrity, and openness. In 2010 the student attained Bolashak International Scholarship which was awarded to high-performing students from Kazakhstan. She has then successfully completed the foundation at Nanyang Technological University in Singapore and obtained a first honor degree in Electronics and Communication

Aftab Hussain

Aftab Hussain received both his Master's degree and Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology under the supervision of Prof. Muhammad Mustafa Hussain. He now works as a faculty member at the International Institute of Information Technology in Hyderabad, India. Research Interest Aftab's research interests include nanoelectronics, fabrication processes especially mask design and epitaxial growth process. Education Profile Ph.D., Electrical Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. (2013-2016) M.S
<> GPU Computing (14)

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.

ISC21 Workshop: Numerical Algorithms and Libraries for Exascale (NAL-X)

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ISC21 KAUST Frankfurt Germany Time CET

Abstract With the hardware technology scaling and the trend on heterogeneous chip design, the existing numerical algorithms and software framework may break down due to load imbalance. There is currently a fundamental mismatch between the underlying hardware architecture with high thread concurrency and the software deployment of numerical libraries, which relies on the traditional bulk synchronous programming model. Numerical software should first squeeze performance out of single node by efficiently running on manycore architectures with processor counts sharing a common memory in the

Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications (SC21 Panel)

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SC21 Saint Louis MO USA

Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications SC21 Panel Time: Tuesday, 16 November 2021, 10:30am - 12pm CST Location: 225-226 (America's Center, St. Louis, MO USA) Abstract With the end of Moore’s Law, the community has witnessed new hardware trends to increase performance. Today, it is not only the traditional x86 and accelerators that are part of computing systems, but also ARM, FPGAs and dedicated processors for DL workloads that equip now pioneering HPC systems. By the end of this decade, we are moving towards an era of extreme scale with “extreme

ECRC brings astronomy back home

1 min read ·
Astronomy research excellence is coming back home to the Middle East, where it took its first steps around 1200 BC. Custome new software developed by the KAUST Extreme Computing Research Center (ECRC) was released at the end of 2017. Named MOAO - Multiple Object Adaptive Optics – the software will contribute to the activities of the world's largest future optical telescope to be deployed in Chile in 2024.

H2Opus: a Performance-Oriented Library for Hierarchical Matrices

1 min read ·
H2Opus: a Performance-Oriented Library for Hierarchical Matrices Download at https://github.com/ecrc/h2opus. Experience our latest HPC software release that implements H 2-Matrix operations on shared-memory systems, possibly equipped with GPU hardware accelerators. The prime target applications for H2Opus are PDE-constrained optimizations. The features of H2Opus include: Generation of matrix structure from a point set and admissibility condition, Construction of a hierarchical matrix given a kernel function, Matrix-vector and matrix-multiple-vector multiplication, Basis orthogonalization

Mixed Feelings About Mixed Precisions: Birds of a Feather at SC22!

1 min read ·
Abstract: Come and share your experiences with the state-of-the-art of mixed-precision techniques! By wisely trading off accuracy, we can mitigate data movement overheads and increase performance of applications, including real-time adaptive optics simulations on ground-based telescopes and genome-wide association study for agricultural genomics. No free lunch, however: these optimizations require support from the software/hardware ecosystem and strong numerical validation. For instance, the tile Cholesky factorization is a fundamental matrix algorithm in solving covariance problems that

Mixed Feelings About Mixed Precisions: Birds of a Feather at SC23!

1 min read ·
Abstract What if we have been oversolving in computational science and engineering for decades? Are low precision arithmetic formats only for AI workloads? How can HPC applications exploit mixed-precision hardware features? This BoF invites the HPC community at large interested in applying mixed precisions into their workflows and discussing the impact on time-to-solution, memory footprint, data motion, and energy consumption. Experts from scientific applications/software libraries/hardware architectures will briefly provide the context on this trendy topic, share their own perspectives, and

Wajih Halim Boukaram

Ph.D. Student, Computer Science
Wajih Halim Boukaram is a PhD candidate in Computer Science. He is studying under the supervision of Professor David E. Keyes.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Nicolas Doucet

Nicolas Doucet is a PhD student in Computer Science, working under the supervision of Professor David Keyes.

Marcin Rogowski

Ph.D. Student, Computer Science

Jens Schneider

Research Scientist, Computer Science
Jens Schneider was a Staff Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Qatar. Education and Early Career Jens Schneider received a diploma equivalent to Master degree in Computer Science from RWTH Aachen in Germany, 2004. He earned his Ph.D. in Computer Science from Technical University Munich, Germany, in 2009 Dr. Schneider joined KAUST in 2009 as a Postdoctoral Research Fellow. Later on, he was promoted to Research

Amani Alonazi

Amani Alonazi is a Visiting Researcher in the Image and Viedo Understanding Lab (IVUL) Research Group working with Professor Bernard Ghanem. Previously, she was a Ph.D. candidate in Computer Science at CEMSE, KAUST, and a member of KAUST's Extreme Computing Research Center, ACM, and SIAM. Areas of expertise and current scientific interests Amani's research focuses on developing novel asynchronous task-based algorithms of solvers in scientific simulations. The core of her work lies within the domain of high-performance computing, which combines performance optimizations, modeling, and

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)
<> MEMS (14)

On Singular Equations Modeling Electrostatic MEMS

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B9 L2 R2325

This talk introduces mathematical models for microelectromechanical systems (MEMS), focusing on the existence and behavior of solutions to equations describing the MEMS device's instability due to electrostatic forces that can lead to singularities in the mathematical equations.

CEMSE Welcomes new Professor: Dr. Hossein Fariborzi

1 min read ·
The CEMSE Division has recently welcomed a new Assistant Professor of Electrical Engineering, Dr. Hossein Fariborzi. Prof. Fariborzi joins from Oracle/Sun Microsystems labs where he was a member of technical staff.

CEMSE Bridging science: IEEE-NEMS best paper award bestowed to CEMSE for scientific boundaries crossing

1 min read ·
Scientific boundary-crossing initiatives are what help scientists stepping across boundary lines that traditionally mark the divide between scientific disciplines.

Device to circuit to system

1 min read ·
The city of the future will be innervated with millions of microscopic sensors. These sensors will need to be virtually invisible, extremely low power and, conversely, extremely powerful from a computing perspective. A team at KAUST is chasing this impossible list of requirements in the form of miniaturized mechanical structures or MEMS.

KAUST alumni receive SPE 2020 Regional Awards

1 min read ·
KAUST alumni Dr. Chinthaka Gooneratne and Dr. Bodong Li both recently received Society of Petroleum Engineers (SPE) 2020 Regional Awards for the Middle East and North Africa Region. Gooneratne won the Regional Drilling Engineering Award, while Li was awarded the Regional Formation Evaluation Award.

Xuecui Zou

Introduction Xuecui Zou is a PhD student under the supervision of Dr. Hossein Fariborzi and Dr. Khaled Salama in Electrical Engineering at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, Department of Computer, Electrical and Mathematical Science and Engineering (CEMSE). Research Interests Research Interests ​Ultra-low power Micro-resonators for logic and sensing applications​ Education ​ The senior student of UESTC(University of Electronic and Science Technology of China)​ Non-KAUST Affiliations ​ University of Electronic Science and Technology of China (UESTC)​​​

Yanfen Zhai

Yanfen Zhai is a Postdoctoral Fellow working under the supervision of Professor Atif Shamim at Integrated Microwaves Packaging Antennas and Circuits Technology (IMPACT) Research Group in King Abdullah University of Science and Technology (KAUST). Research Interests Yanfen's research interests include Nano-Photonics and Nanotechnology, Antenna design, integration and miniaturization techniques, On-Chip Mid-Infrared Gas Sensor, On-Chip Ultra - Wideband Optical Switch Matrix (MEMS), and Quantum Single-Photon Source (Nonlinear optics). Patents: 1) Y. Zhai, W. Zhang, and Y. Huang, “A kind of

Sally Ahmed

Sally Ahmed was a Ph.D. student in Electrical Engineering in the Computer, Electrical and Mathematical Science & Engineering division of King Abdullah University of Science and Technology. She was a member of the Integrated Circuits and Systems research group (ICS) from May 2016 to December 2020 and her thesis was supervised by Prof. Hossein Fariborzi. Sally obtained her Master's degree in Electrical Engineering from KAUST in 2013 and has a Bachelor's degree in Electronics Engineering from American University in Cairo (AUC). Research Interests Sally's research interests include MEMS Devices

Mohammed Asadullah Khan

Mohammed Asadullah Khan received the B.E. degree from Osmania University, Hyderabad, India, in 2008, and the M.E. degree from the Indian Institute of Science, Bangalore, India, in 2010. He was a Design Engineer with Intel Integrated Platform Research Labs from 2010 to 2013. He is currently pursuing a Ph.D. degree in electrical engineering from the Sensing, Magnetism, and Microsystems Group, under the supervision of Prof. Jürgen Kosel. He has co-authored 11 journal articles after joining KAUST. Also, while working at KAUST, he presented his research at various international conferences. He is

Khalil Moussi

Postdoctoral Research Fellow, Electrical and Computer Engineering
Selected ​Publications K. Moussi and J. Kosel, "3-D Printed Biocompatible Micro-Bellows Membranes," Journal of Microelectromechanical Systems, vol. 27, no. 3, pp. 472-478, June 2018. K. Moussi, A. Bukhamsin, and J. Kosel, "Implantable 3D Printed Drug Delivery System," IEEE Transducers/EUROSENSORS, June 2019. K. Moussi, M. AlDajani, and J. Kosel, "Miniaturized Drug Delivery System for Biomedical Applications," IEEE NEMS Conference, April 2019. K. Moussi, S. Naifar, O. Kanoun, S. Bradai. “Analytical Model of Magnetostrictive/Piezoelectric Laminate Composites”, 5th IMEKO TC19 SYMPOSIUM, pp. 51-55

Hussein Hussein

Postdoctoral Research Fellow, Electrical and Computer Engineering
Hussein Hussein is a Postdoctoral Fellow at the Integrated Circuits and Systems Research Group under the supervision of Professor Hossein Fariborzi in King Abdullah University of Science and Technology (KAUST). Hussein obtained his PhD in engineering sciences and microsystems from University of Franche-Comté. He has a mechanical engineering degree from the Lebanese University and a Master degree in Mechatronic systems from University of Compiegne . Research Interests Mechanics and Mechatronics at macro and micro scale. Modeling, design and fabrication of microsystems and MEMS structures

Ian Foulds

Ian Foulds was a founding faculty member in electrical engineering at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, a graduate-only research university on the shores of the Red Sea that was ranked first in the world for citations per faculty in the QS World University Rankings® 2015/16. Prior to KAUST, Ian did a NSERC post-doctoral fellowship at the University of Victoria after graduating from Simon Fraser University. Ian leads the Electromechanical Microsystems & Polymer Integration Research (EMPIRe) Group developing MEMS and Microfluidic systems and the

Hossein Fariborzi

Adjunct Professor (former), Electrical and Computer Engineering
Hussain Fariborzi is an Assistant Professor of Electrical and Computer Engineering (ECE) and the Principal Investigator of the Integrated Circuits and Systems (ICS) Research Group. Fariborizi addresses problems in ultra-low power Integrated Circuits and Systems, MEMS/NEMS IC design and fabrication, as well as novel switching devices for next-generation ICs. Education and early career Fariborzi holds a Ph.D. in Engineering from the Massachusetts Institute of Technology (MIT), 2013. He worked at Intel, then was an RF Design Engineer at Nokia and joined Oracle/Sun Microsystems Labs. Areas of

Abdullah Bukhamsin

Abdullah Bukhamsin is a Ph.D. student at the Sensors Lab, in the Bioengineering Department, under the supervision of Professor Khaled Nabil Salama at KAUST. Prior to this, Abdullah obtained an M.S. Degree in Engineering under the supervision of Professor Jurgen Kosel at King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdullah Bukhamsin received his B.Sc. degree in Bioengineering from Rice University (Houston, Texas) in 2018. Following that, he completed his M.Sc. degree in Electrical Engineering from King Abdullah University of Science and Technology
<> scientific computing (14)
Georgios Grekas

Georgios Grekas

Scalable Hierarchical Algorithms for eXtreme Computing Workshop

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KAUST

The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.

Deep Learning and Scientific Computing

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B9 L2 R2322

In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

PhD Defense of Tariq AlTurkestani

1 min read ·
Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems PhD Dissertation Defense by Tariq Alturkestani PhD Student, Computer Science Thursday, July 9, 16:00 - 17:00, https://kaust.zoom.us/j/94054511362 Contact Person: Tariq Alturkestani The execution rate of floating-point operations has typically increased by an order of magnitude every four years during the last 30 years of modern comput ing. This exponential growth in terms of computational power has benefited from the hardware technology scaling. However, memory and storage systems have not maintained

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Theodoros Katsaounis

Research Scientist, Applied PDE Group
Short Bio Dr. Theodoros Katsaounis is a Professor of Mathematics at the University of Crete. He received his Ph.D in 1994 for the Univ. of Tennessee USA, in applied mathematics. After his postdoctoral studies in the Ecole Normale Superieure of Paris, he joined the Dept. of Applied Mathematics of Univ. of Crete, Greece as an Assistant professor in 2003. Dr. Katsaounis was a Research Scientist at KAUST from 2015 to 2020. His research interests are in the area of applied and computational mathematics. Education Profile Ph.D Mathematics, Univ. of Tennessee, USA, 1994 M.Sc. Mathematics, Univ. of

Steven Dufour

Steven Dufour is a Former Visiting ​Associate Professor from École Polytechnique de Montréal, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Steven's research activities revolve around the development of numerical methodologies for the modeling of free surface flows that can be found in various industrial applications. The main challenge associated with the numerical modeling of multifluid flows is to accurately locate the interfaces between each fluid. More complex flows, such as the flow of

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Haneen Mohammed

Haneen Mohammed was a Master student in Computer Science Program under the supervision of Professor Markus Hadwiger at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, she’s preparing her doctoral in Computer Science at Columbia University in the City of New York, United States. Education and Career Haneen earned her bachelor degree in Electrical and Computer Engineering from Effat University in Jeddah in 2015. She joined KAUST and received her master degree in Computer Science in 2017. Research Interest ​Her research interests are mainly

Grigorios Kounadis

Short Bio Grigorios Kounadis is a Postdoctoral Research Fellow at the Applied PDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He received a bachelor degree in Mathematics from National and Kapodistrian University of Athens (NKUA), Greece, in 2006, and graduated with a Ph.D degree in Applied Mathematics from NKUA in 2020. He joined KAUST and the Applied PDE group in March 2021. Grigorios' research interests lie in the area of numerical analysis and scientific computing

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.
<> Time Series (14)

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

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B1 L4 R4102

Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.

Yongxin Zhu

Ph.D., Statistics
Yongxin Zhu was a MS student in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career "I'm currently working as a data scientist. The knowledge I acquired at KAUST helps me to be sensitive about the potential usage of data and to analyze data in a rigorous and systematic way." - Yongxin Zhu Yongxin obtained his Bachelor of Science degree in Business and Finance (2017) from the New York University Shanghai, China. In 2018, he graduated with his Master of Science

Wagner Barreto-Souza

Research Scientist, Statistics
Wagner Barreto-Souza is a Research Scientist of the KAUST Biostatistics Group headed by Prof. Hernando Ombao. Early Career Professor of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. (2013-2020) Education Profile Ph.D. in Statistics. Universidade de São Paulo, SP, Brazil. (2012) M.S. in Statistics. Universidade Federal de Pernambuco, Recife, Brazil. (2008) B.S. in Statistics. Universidade Federal de Pernambuco, Recife, Brazil. (2006)

Siti Balqis Samdin

Postdoctoral Research Fellow, Statistics
Siti Balqis Samdin is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Siti Balqis obtained her Ph.D. and Bachelor of Engineering in Biomedical Engineering from Universiti Teknologi Malaysia (UTM) in 2017 and 2011, respectively. She worked as Research Assistant at UTM before joining KAUST in 2018. Research Interest Her research interest primarily lies in biomedical signals and image processing using statistical methods such as time-series

Sarbojit Roy

Postdoctoral Research Fellow, Statistics

Raanju Ragavendar Sundararajan

Postdoctoral Research Fellow, Statistics
Raanju Sundararajan is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Raaju received his Ph.D. degree in Statistics in 2018 from the Texas A&M University, Texas, United States. Research Interest His research interests lie in fields such as nonstationary time series, frequency domain, and nonparametric methods. Editorial Positions Referee for journals such as Statistics in Medicine and Brain Connectivity.

Paolo Victor Redondo

Ph.D., Statistics
Ph.D. Degree in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser and Prof. Hernando Ombao.

Oleg Gorshkov

Visiting Researcher, Statistics
Oleg Gorshkov is a Visiting Researcher working in collaboration with Professor Ombao at the Biostatistics Group of the Statistic Program of KAUST. Research Interest Oleg's research interests lie in fields such as fractal analysis and chaos theory; focusing on the following main topics: analysis of bio-medical time series signals (R-R intervals, stabilograms, encephalograms, step width and stride length time series ); bio-medical images (thermograms). Editorial Position Reviewer, IEEE Signal Processing Letters Reviewer, IET Systems Biology

Meini Tang

M.S. Student, Statistics
Meini Tang is an MS student in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Meini obtained her Bachelor of Science in Applied Statistics (2018) from Sun Yat-sen University, Guangzhou, China. Then, she joined the MS/Ph.D. program in Statistics at KAUST in 2017. Research Interest Her research interests lie in fields such as t ime series analysis, brain-network analysis, Bayesian statistics, and machine learning.

Matheus B. Guerrero

Ph.D. Student, Statistics
Matheus was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), doing research under the joint supervision of Prof. Raphaël Huser and Prof. Hernando Ombao. Matheus successfully defended his PhD thesis entitled " Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data" on April 4th, 2023; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Prof. Hernando Ombao (co-chair), Miguel de Carvalho (external examiner from the University of

Maicon J. Karling

Postdoctoral Research Fellow, Statistics
Education B.Sc. in Mathematics (2014) - University of Coimbra, Portugal. B.Ed. in Mathematics (2015) - Unisinos, Brazil. M.Sc. (2017) and Ph.D. (2021) in Mathematics: Probability and Statistics - Federal University of Rio Grande do Sul, Brazil. Research Interests Goodness-of-fit tests, multivariate statistics, skewed distributions, and time series.

Hernando Ombao

Professor, Statistics
KAUST Professor of Statistics Hernando Ombao is an expert in time series models and their applications to designed medical experiments on complex biological processes.

Guillermo C. Granados Garcia

Ph.D. Student, Statistics
Guillermo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Guillermo obtained his Bachelor of Science in Actuary by the Universidad Nacional Autónoma de México. Then, he joined the MS/Ph.D. program in Statistics at KAUST in 2017. In 2018, he graduated his Master of Science in Statistics at KAUST. Research Interest His research interests lie in fields such as stochastic financial models, actuarial sciences, biostatistics, time series

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.
<> wireless networks (14)

Towards Enhancing Security in Intelligent Surface-enabled Wireless Networks

Prof. Galymzhan Nauryzbayez, Nazarbayev University, Kazakhstan

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B2 5220

Abstract The rampage of incessant cyber attacks have caused the disclosure of billions of users’ private data. In response, various data privacy laws and regulations have emerged, forcing the academia and industry to change their practice and bringing the demand for large-scale secure wireless networks to the spotlight. Such a demand, however, cannot be met by the state-of-the-art cryptographic techniques. To narrow the gap, recent years have seen rapid progress in physical layer security (PLS) solutions. In this talk, I will present our research on understanding and addressing the security

Prof. Osama Amin Elevated To Senior Member Of IEEE

1 min read ·
Dr. Osama Amin has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

A breath of fresh information to help diagnosis

1 min read ·
A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.

Yongqiang Zhang

Yongqiang Zhang is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST).

Tareq Al-Naffouri

Professor Al-Naffouri's research focuses on designing and analyzing wireless communication systems, particularly spectrum sharing, cognitive radio, energy efficiency and security within the context of advanced technologies like 5G.

Taha Bouchoucha

Taha Bouchoucha was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Sameh Sorour

Postdoctoral Research Fellow (former), Communication Theory Lab
Postdoctoral Research Fellow at King Abdullah University of Science and Technology (KAUST),Assistant Professor at King Fahd University of Petroleum and Minerals (KFUPM) since September 2013.

Ruslan Zhagypar

MS/PhD Student, Electrical and Computer Engineering , King Abdullah University of Science and Technology

Osama Hassan

Osama Hassan is a Master's student studying under the supervision of Prof. Mohamed-Slim Alouini in the Communications Theory Lab (CTL) at King Abdullah University of Science and Technology (KAUST). Education Osama received his Bachelor of Science in Engineering (BSE) in Electrical Engineering from Princeton University in 2018, with a concentration in Security and Privacy, and a certificate in Applications of Computing. Early Career At Princeton, Osama conducted research at the Houck Lab for Quantum Computing and Condensed Matter Physics, where he worked on designing and testing quantum
Mostafa Emara

Mostafa Mohamed Lotfy Emara

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
PhD Student, Electrical and Computer Engineering at KAUST (King Abdullah University of Science and Technology)

Mohammed E. Eltayeb

Intern at King Abdullah University of Science and Technology (KAUST),

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Bayan Aloqaybe

MS Degree, Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST)
<> wireless sensor networks (14)

Yinghao Liu

Yinghao Liu was a CTL lab member and graduated from KAUST. Education ​B.S. Major in Automation in the School of Information Science and Technology, Minor in Communication in the Department of Science and Technology Communication, University of Science and Technology of China (USTC), China, (September 2012 - June 2016).

Taha Bouchoucha

Taha Bouchoucha was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Samet Guler

Postdoctoral Research Fellow, Electrical and Computer Engineering
Samet Güler (M’16) received the B.S. degree in electrical and electronics engineering from Eskisehir Osmangazi University, Turkey, in 2010, the M.S. degree in electrical and electronics engineering from Bilkent University, Turkey, in 2012, and the Ph.D. degree in mechanical and mechatronics engineering from University of Waterloo, Waterloo, ON, Canada, in 2015. He worked as a short term Researcher at the University of Waterloo in 2016. He is currently a Postdoctoral Fellow at the Robotics, Intelligent Systems and Control Lab in King Abdullah University of Science and Technology, Kingdom of

Osama Bushnaq

Ph.D. degree in electrical engineering at the King Abdullah University of Science and Technology (KAUST)

Muhammad Junaid Farooq

Muhammad Junaid Farooq was a CTL lab member and graduated from KAUST. Then, he joined New York University as a PhD student. Selected Publications ​ Conference Publications 1. M. J, Farooq, M. Hussain, J. Qadir and A. Baig, "A game-theoretic spectrum allocation framework for mixed unicast and broadcast traffic in cognitive radio networks", in Proc. of IEEE International Conference on Local Computer Networks (LCN), Sydney, Australia, Oct. 2013. Education M.S. in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia since September 2013. B.S. in

Mohammed Ahmed

Postdoctoral Research Fellow (former), Information Science Lab
post-doctoral fellow at L'École de technologie supérieure (ETS) de Montréal (Mtl) where he started on February 2014. he works with Prof. Georges Kaddoum.

Mohammad Saeed Abuzaid

​Mohammad Saeed Abuzaid was a CTL lab member and graduated from KAUST. Then, he joined McKinsey as an engineer. Education MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia since September 2012. B.S. in Electrical Engineering, Jordan University of Science and Technology (JUST), Irbid, Jordan (September 2006 - January 2011).

Matthew Debont

Matthew Debont obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Research Interests Matthew's research interests included Wireless Sensor Networks, Computer Networking, Agile Software Development, and Information and Communication Technology. Thesis title: " Event Localization in Underwater Wireless Sensor Networks using Monitoring Courses." Matthew's work contributes to improving the UK's evidence-based policies in the environmental sector. More specifically, his current project

Lokman Sboui

Lokman was a ​Ph.D. student at King Abdullah University of Science and Technology (KAUST) from June 2013-December 2017. Then, he joined Ecole de Technologie Supérieure (ETS), Quebec as a postdoctoral research fellow after graduation. Scientific and Professional Memberships Institute of Electrical and Electronics Engineers (IEEE). The reviewer of many papers in IEEE transactions and conferences. Tunisian Federation of Equestrian. Tunisian Federation of Subaquatic Activities.​ Education Profile Ph.D. in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal

Chun Pong Lau

Chun Pong Lau obtained both his M.S. degree and Ph.D. degree from Computer Science at King Abdullah University of Science and Technology (KAUST). His Ph.D. thesis supervisor was Prof. Basem Shihada. Currently, Chun is working as a Researcher at Huawei, Hong Kong. Education Profile Ph.D., Computer Science, KAUST, Saudi Arabia M.Sc., Computer Science, KAUST, Saudi Arabia B.Sc., Computer Engineering, HKUST, Hong Kong Scientific and Professional Membership ​​​​IEEE, Student Member Research Interests Chun's research interests included video multicasting and broadcasting, superposition coded

Anas Daghistani

Anas Daghistani obtained his Master's Degree in Computer Science under the supervision of Prof. Basem Shihada from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Anas received a B.Sc. degree in Computer Engineering from Umm Al-Qura University, Makkah, Saudi Arabia. Currently, he is a Ph.D. student at Purdue University. Master Thesis title: " Green-Frag: Energy Efficient Frame Fragmentation Scheme for Wireless Sensor Networks". Research Interest Anas's research interests cover a wide range of topics in computer networking such as wireless sensors, MIMO systems

Ammar Meer

Ammar Meer obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Thesis title: " H-Seda: Partial Packet Recovery with Heterogeneous Block Sizes for Wireless Sensor Networks."

Ahmad Al-Showail

KAUST alumnus Ahmad Showail has recently been appointed dean of the College of Computer Science and IT at the new non-profit university in Madinah, University of Prince Mugrin ( UPM). Showail graduated from the computer engineering department with honors from King Fahd University of Petroleum and Minerals ( KFUPM) in 2005. The Madinah-based alumnus started his career as a computer engineer in the Saudi Basic Industries Corporation (SABIC) before developing an interest in academia. This led him to teach at Yanbu University College at the Royal Commission in Yanbu before joining KAUST in 2009 as

Abdullah Halafi

Abdullah Halafi obtained his Ph.D. degree in Electrical Engineering under the supervision of Professor Basem Shihada at Networking Research Lab in King Abdullah University of Science and Technology (KAUST), in 2018. Abdullah Al-Halafi received the B.Sc. degree in electrical engineering from the King Fahd University of Petroleum and Minerals, Saudi Arabia, in 2003, the M.Sc. degree in electronics and electrical engineering from the University of Glasgow, U.K., in 2006. He was involved in engineering and project management of various large-scale communications and control systems projects at
<> computational methods (13)

Spatial Dataflow is all you need!

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B9 L2 R2325

Jian Weng's research, inspired by spatial dataflow, proposes a unified hardware design and implementation framework that leverages a familiar software programming paradigm, enabling concise and efficient designs while significantly reducing code complexity and achieving near-handcrafted performance, with future applications in ML inference systems targeting memory bandwidth limitations.

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Arwa Bin Raies defends her thesis proposal

1 min read ·
Arwa Bin Raies, a Ph.D. candidate under the supervision of Professor Vladimir Bajic, defended her thesis proposal on May 14, 2018.

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in
<> Federated learning (13)

Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity

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B5 L5 R5209

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

EF-BV: distributed optimization with compressed communication

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B9 L2 R2322 H1

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Making federated learning practical and confidential

Dr. Ruichuan Chen, Distinguished Member of Technical Staff and a Tech Lead, Nokia Bell Labs

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B4 L5 R5209

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address the privacy concerns, FL systems need to protect the clients' data from being revealed during training, and also control data leakage through trained models when exposed to untrusted domains. However, existing FL systems (with distributed differential privacy) work impractically in the presence of client dropout, resulting in either poor privacy guarantees or degraded training accuracy. In addition, existing FL systems focus on safeguarding the privacy of training data, but not on protecting the confidentiality of the models being trained, which are increasingly of high business value. In this talk, I will present two pieces of our recent work that aim to address these aforementioned issues.

FADO: A Federated Learning Attack and Defense Orchestrator

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B5 L5 R5209

Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.

Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

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B4 L5 R5220

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Ziwu Liu

M.S. Student, Computer Science
My passion for the evolving landscape of technology has driven me to focus on the intricate interplay between resilient computing, federated learning, edge computing, and machine learning systems. My research aims to seamlessly integrate edge computing with federated learning to create resilient and adaptive machine learning systems that can operate effectively even in resource-constrained settings.

Rafal Szlendak

Rafał Szlendak is an MS/PhD student in the AMCS program under the supervision of Prof. Peter Richtarik. His biggest passions are research, weightlifting and travelling. His career path is motivated by his endless passion for self-development and discovery. Education and Early Career Rafał Szlendak has obtained his Bachelor's degree in Mathematics and Statistics from University of Warwick Research Interest In his research Rafał focuses on stochastic gradient descent algorithms for large-scale machine learning problems. His primary goal is developing state-of-the-art methods with sound

Norah S. Alballa

Ph.D. Student, Computer Science
Education Profile Ph.D. in Computer Science, CEMSE, KAUST, 2022-present M.S. in Computer Science, Portland State University, 2016 B.S. in Information Technology, King Saud University (KSU), 2010

Laurent Condat

I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Konstantin Burlachenko

Ph.D. Student, Computer Science
Konstantin's research focuses on large-scale mathematical optimization systems for machine learning and artificial intelligence, especially those requiring high-performance computing approaches.

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Boris Radovic

Boris Radovic is a Ph.D. candidate in the computer science program, under the supervision of Professors Marco Canini and Veljko Pejović, and is a member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab. He holds a Bachelor of Science (B.Sc.) in Computer Science and a Master of Science (M.Sc.) in Data Science, both obtained from the Faculty of Computer and Information Science at the University of Ljubljana, Slovenia. During his academic journey, Boris gained experience across diverse computer science domains through his long-term collaboration with the Laboratory
<> Green communications (13)

Anas Daghistani Received The Best Paper Award At (IEEE WiMob, 2013)

1 min read ·
Anas Daghistani, MSc Alumni in the Computer Science (CS) Program at the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) division received the best paper award at the International Conference on Wireless and Mobile.

Dr. Hesham ElSawy recognized as Exemplary Reviewer by IEEE Transactions on Communications

1 min read ·
Dr. Hesham ElSawy was recognized by the IEEE Transactions on Communications (TCOM) as an Exemplary Reviewer. Less than 2% of all IEEE TCOM Reviewers in 2015 got this recognition.

Dr. Hesham ElSawy Elevated to Senior Member of IEEE and Recognized as an Exemplary Reviewer by the IEEE Transactions on Communications

1 min read ·
Dr. Hesham ElSawy has been elevated to the rank of Senior Member of IEEE, the world's largest technical professional organization for the advancement of technology. The achievement is considered a significant honor since it is granted only to researchers who have shown a significance and continuous performance over a period of five years in professional practice. At present, only an 8% of 400,000 IEEE members have received this honor because of their superior professional achievements.

Hesham ElSawy wins IEEE Communication Society

1 min read ·
The Internet of Everything in quickly growing and vastly enlarging its spectrum of applications. At the same time, traffic demand per-user is increasing dramatically. It is estimated that by 2020 there will be more than 50 billion devices connected. The cellular support infrastructure should be developed accordingly, making of network performance analysis a critical task. The existing elementary probability methods are currently inefficient when it comes to detecting mobility faults and interference.

Hesham ElSawy gains ISWCS Best Scientific Contribution Award 2017

1 min read ·
The imminent era of the smart world is foreseen to involve massively many power and computationally constraint wireless devices with sporadic traffic patterns. The surging Internet of things (IoT) and cyber physical systems (CPS) are clear examples that rely on massive wireless networks. Hence, realizing such extensive wireless connectivity is mandatory to unlock the potentials and reap the benefits of the foreseen smart era.

Rachad Atat

Rachad Atat was a CTL lab member and graduated from KAUST. Then, he joined the KTH Royal Institute of Technology (KTH) as a PhD student. Selected Publications Book Chapters: M. Z. Shakir, R. Atat, and M. -S. Alouini, "Interference suppression capabilities of smart cognitive-femto networks (SCFN),” Chapter in Self-Organization and Green Applications in Cognitive Radio Networks, Al-Dulaimi, A., Cosmas, J., and Mohammed, A. (Eds), IGI Global, 2012. M.S. Thesis: R. Atat, "Cooperative Content Distribution over Wireless Networks for Delay and Energy Efficiency," King Abdullah University of Science

Mustapha Benjillali

Postdoctoral Research Fellow, Communication Theory Lab
In June 2009, Mustapha Benjillali joined the Electrical Engineering Program, Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, KSA, as a Postdoctoral Research Fellow in association with Prof. Mohamed-Slim Alouini. Non-KAUST Affiliations Professor, Communications Systems Department, INPT, Rabat, Morocco. Education Profile Ph.D. in Telecommunications from INRS, Montreal, QC, Canada, 2009. M.Sc. in Telecommunications from INRS, Montreal, QC, Canada, 2005. Diplome d’Ingenieur in Mobile Communications from INPT, Rabat, 2003.

Muhammad Mehboob Fareed

Postdoctoral Research Fellow, Communication Theory Lab
Muhammad Mehboob Fareed is now a Technical Lead in Mircom, Toronoto, Canda. Selected Publications Book Chapters: M. Uysal and M. M. Fareed, “Cooperative Diversity Systems for Wireless Communication,” Chapter in Handbook on Information and Coding Theory, I. Woungang, S. Misra and S. C. Misra (Eds.), World Scientific, 2010. PhD Thesis: M. M. Fared, "Optimum Power Allocation for Cooperative Communications," University of Waterloo, Waterloo, Canada, Aug. 2009. Journal Publications: M. M. Fareed and M. Uysal, “On relay selection for decode-and-forward relaying,” IEEE Transactions on Wireless

Jian Qi

Postdoctoral Research Fellow, Communication Theory Lab
Jian Qi is an Assistant Professor in University of Reading, Berkshire, UK. Selected Publications Book Solutions Manual: T. M. Duman and A. Ghrayeb, Coding for MIMO Communication Systems, John Wiley and Sons, 2007. (Solutions manual prepared by Jian Qi and Xiang NianZeng) Ph.D. Thesis: J. Qi, “Analysis and Compensation of Channel and RF Impairments in MIMO Wireless Communication Systems,” Institut national de la recherche scientifique (INRS), Montreal, QC, Canada, Apr. 2011. M.S. Thesis: J. Qi, “Research and Realization of Capacity Dimensioning Algorithm for High-Speed Wireless Links,”

Hesham ElSawy

Postdoctoral Research Fellow, Communication Theory Lab
From April 2014 to June 2018 Hesham ElSawy was a postdoctoral fellow in the Communication Theory Lab (CTL), at the Computer, Electrical, and Mathematical Sciences and Engineering division (CEMSE), King Abdullah University for Science and Technology (KAUST), Kingdom of Saudi Arabia. He is now an assistant professor in King Fahd University of Petroleum and Minerals (KFUPM), Eastern Province, KSA Selected Publications Ph.D. Thesis H. ElSawy, “Modeling, Analysis, and Optimization of Random Wireless Networks: Stochastic Geometry Approach,” University of Manitoba, Winnipeg, MB, Canada, March 2014. M

Amr M. Abdelhady

Postdoctoral Research Fellow, Communication and Computing Systems Lab
Amr is a Postdoctoral Fellow in the Communication & Computing Systems Laboratory (CCSL) supervised by Professor Ahmed M. Eltawil at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amr earned his bachelor degree in communications and computer engineering from Cairo University Faculty of Engineering (CUFE) in 2012. He worked in Cairo University for Wireless Studies (CWS) center after graduation. He joined King Abdullah University of Science and Technology in 2014 and earned his Master degree in 2016 and his PhD in 2021. Research Interest Amr ‘s research is

Ahmad R. Dhaini

Postdoctoral Research Fellow, Networking Research Lab
Ahmad R. Dhaini was an off-campus Postdoctoral Fellow from University of Waterloo, Canada, under the supervision of Prof. Basem Shihada at the Networking Research Lab, King Abdullah University of Science and Technology (KAUST). Currently, he is an Assistant Professor of Computer Science at the American University of Beirut (AUB). He received his B.Sc. in Computer Science from AUB in 2004. He then earned his M.Sc. degree in Electrical and Computer Engineering from Concordia University, Montreal, Canada in 2006. His Master’s dissertation was nominated for the best thesis award. In 2006-2007, he

AbdulAziz Al-Orainy

Visiting Researcher (former), Electrical and Computer Engineering
Visiting Researcher at King Abdullah University of Science and Technology (KAUST), Assistant Professor at King Abdulaziz City for Science and Technology (KACST)
<> Physical layer security (13)

Dr. Zouheir Rezki promoted to Senior Member of IEEE

1 min read ·
Dr. Zouheir Rezki (Research Scientist in Communication Theory Lab) has been elevated to the rank of Senior Member of IEEE.

Zouheir Rezki

Senior Research Scientist, Communication Theory Lab
Zouheir Rezki is now an Assistant Professor in the Electrical and Computer Engineering Department at University of Idaho. During November 2009 to July 2012 , he was a Senior Research Scientist in the Computer, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST). Selected Publications Ph.D. Thesis Z. Rezki, “Performance Limits of Multi-Antenna Communication Systems: Diversity Multiplexing Tradeoff and Noncoherent Capacity,” University of Montreal, Ecole Polytechnique, Montreal, QC, Canada, Aug. 2008. M.S. Thesis Z. Rezki,

Yao GE

Visiting Scholar with the Department of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST)

Xinyu Zhou

Xinyu Zhou was a Visiting B.S. Student in the CTL. Then, he joined University of Maryland, USA as a PhD Student. Selected Publications Xinyu Zhou, Zouheir Rezki, Basel Alomairy and Mohamed-Slim Alouini, “Achievable Rates of Secure Transmission in Gaussian MISO Channel with Imperfect Main Channel Estimation” with full paper version submitted to IEEE Transactions on Wireless Communications and conference version submitted to IEEE CNS. Education Research internship in Electrical Engineering department, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (September

Waqas Aman

Visiting Student (former), Electrical and Computer Engineering
Remote Internship at King Abdullah University of Science and Technology (KAUST).

Redha M. Radaydeh

Research Scientist, Communication Theory Lab
From Sept. 2009 to Sept. 2012, Redha M. Radaydeh was a research fellow at KAUST and then an associate research scientist at TAMU Qatar. From Sept. 2012 to Aug. 2016, he worked at Alfaisal University as an associate professor of EE. Research Interests • Overlaid multi-tier HetNets. • Performance analysis of UA communications. • Resource sharing, distribution, and allocation techniques. • Interference management and mitigation schemes. • D2D and small-cell communications in 5G cellular networks. • Mixed RF and FSO communications. • Physical layer security. • Cooperative relaying systems. •

Mustafa Kishk

Postdoctoral Research Fellow, Electrical and Computer Engineering
Mustafa Kishk is a Postdoctoral Research Fellow under the supervision of Prof. Slim Alouini at the Communication Theory Lab in King Abdullah University of Science and Technology (KAUST). Education and Early Career Mustafa Kishk received his Ph.D. in Electrical Engineering from the Bradley department of Electrical and Computer Engineering at Virginia Tech under the supervision of Prof. Harpreet Dhillon in 2018. He received his B.Sc. and M.S. degree in Electronics and Electrical Communications Engineering from Cairo University, Egypt, in 2013 and 2015 respectively. Research Interest Mustafa’s

Mudassir Masood

Ph.D. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST). ,Assistant Professor, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Mohamed A. Gaafar

Mohamed A. Gaafar was a CTL lab member and graduated from KAUST. Then, he joined the Technical University of Berlin as a PhD student. Selected Publications Selected Conferences 1. M. Gaafar, O. Amin, W. Abediseid, and M.-S. Alouini, “Sharing the licensed spectrum of full-duplex systems using improper Gaussian signaling,” in Proc. IEEE Global Commun. Conf. (GLOBCOM), San Diego, USA, Dec. 2015, pp. 1–6. 
 2. M. Gaafar, M. G. Khafagy, O. Amin, and M.-S. Alouini, “Improper Gaussian signaling in full-duplex relay channels with residual self-interference,” in Proc. IEEE Int. Conf. Commun. (ICC)

Marwen Zorgui

Marwen Zorgui was a CTL lab member and graduated from KAUST. Then, he joined the University of California Irvine at a PhD student. Selected Publications M. Zorgui, Z. Rezki and M.-S. Alouini, “On achievable Rates of Interference and Cognitive Channels with a Relay”, accepted in International Symposium on Personal, Indoor, and Mobile Radio Communications 2015 (PIMRC’2015). M. Zorgui, Z. Rezki, B. Alomair, E.A. Jorswieck and M.-S. Alouini, “Secret-Key Agreement over Spatially Correlated Multiple-Antenna Channels in the Low-SNR Regime”, accepted in IEEE Conference in Communications and Network

Kihong Park

Senior Research Scientist, Electrical and Computer Engineering
Kihong Park is a Senior Research Scientist at Electrical Engineering Program, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career He received Joint M.Sc. and Ph.D. degree in the School of Electrical Engineering, Korea University, Korea in 2011. He was a visiting student in Texas A&M University at Qatar for one year in 2008-2009. He joined KAUST as a Postdoctoral Fellow in April, 2011. Since

Jingping Qiao

Visiting Scholar, Communication Theory Lab
Jingping Qiao is a visiting scholar at King Abdullah University of Science and Technology (KAUST), working with Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Jingping Qiao obtained her BS degree in Communication Engineering from Inner Mongolia University of Science and Technology (IMUST) in 2012. After that, she obtained her Ph.D. degree in Communication and Information Systems from Shandong University in 2018. Before joining KAUST, she worked as a lecturer at Shandong Normal University (SDNU). Research Interest Jingping Qiao is focusing in the

Hongjiang Lei

Visiting Scholar, Communication Theory Lab
Hongjiang Lei now is an Associate Professor in School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing, China. Selected Publications ​ Ph.D. Thesis H. Lei, “Modeling and Analysis of the Physical Layer Security over Wireless Fading Channels”, College of Opto-Electronic Engineering, Chongqing University, Chongqing, China, Dec. 2015. Journal Publications 1. H. Lei, H. Zhang, I. S. Ansari, C. Gao, Y. Guo, G. Pan, and K. A. Qaraqe, “Secrecy outage performance for SIMO underlay cognitive radio systems with generalized selection combining
<> Random Matrix Theory (13)

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

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B1 L4 R4214

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches

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B1 L3 R3119

Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.

Measurement Selection: A Random Matrix Theory Approach

1 min read ·
Khalil Elkhalil , Student Member, IEEE, Abla Kammoun, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, and Mohamed-Slim Alouini , Fellow, IEEE Abstract This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low

Xiaoke Yang

Visiting Scholar, Information Science Lab
PhD candidate at King Abdullah University of Science and Technology. Visiting Scholar, Information Science Lab

Qurrat-Ul-Ain Nadeem

Qurrat-Ul-Ain Nadeem selected while at KAUST as one of the 2018 Marconi Society Paul Baran Young Scholars and she currently is a Postdoctoral Fellow at the School of Engineering, the University of British Columbia, Okanagan Campus, working with Professor Anas Chaaban in the Communication Theory Lab. Education and Early Career Qurrat obtained her Master of Science and Ph.D. degrees in Electrical Engineering from KAUST in June 2015 and December 2018 respectively. Before joining KAUST, she completed her Bachelor of Science degree in Electrical Engineering from Lahore University of Management

Muath Mahdi

Postdoctoral Fellow with the Electrical Engineering Department, King Abdullah University of Science and Technology (KAUST)

Mohamed A Suliman

Visiting Scholar (former), Information Science Lab
MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London..

Mahmoud Hassan Ghazal

Visiting student at KAUST, under the supervision of Professor Tareq AlNaffouri ,PhD Student, Electrical and Computer Engineering ,King Abdullah University of Science and Technology

Lama Burhan Niyazi

Ph.D. degree in the Communication Theory Lab (CTL) under the joint supervision of Professor Mohamed-Slim Alouini and Professor Tareq Al-Naffouri , King Abdullah University of Science and Technology (KAUST)

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering
PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Ismail Ben Atitallah

Ismail Ben Atitallah was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Ikram Boukhedimi

Ikram Boukhedimi was born in Blida, Algeria. She received an Electrical Engineering degree from the Ecole Polytechnique d'Alger, Algeria, in 2013 and an M.S. in Mathematics, Control and Electrical Engineering from Universite de Poitiers, France in 2014. and she earned a Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology in June 2019, Saudi Arabia in Professor Mohamed Slim Alouini’s Communication Theory Lab in Electrical Engineering research group. Her research interests include random matrix theory approaches applied to wireless communication systems

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory
<> Visible light communications (13)

Laser lighting and its application in visible light communications

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B9 L2 R2322

This talk provides an overview of the latest laser-based lighting and Gbit/s VLC and underwater communications. Recent progress of visible wavelength high-speed transmitters and receivers will also be discussed.

Visible Light Communications in the UK: Recent Progress and Future Outlook

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B2 L5 R5220

Abstract Visible light communications (VLC) is a hot topic in internet access networks, developing rapidly over the last decade. It has become one of the main candidate technologies to complement existing radio infrastructure in 5G and beyond technologies due to its inherent advantages such as high bandwidth and room-to-room security. In this talk, I will discuss several of the key developments that have occurred within the context of the UK research community, with particular focus on polymer photoactive components based on research that has occurred through my time at University College

Fabrication and Characterization of GaN-Based Superluminescent Diode for Solid-State Lighting and Visible Light Communication

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KAUST

This dissertation is devoted to the fabrication and electrical and optical characterization of a new class of III-nitride light-emitter known as superluminescent diode (SLD). SLD works in an amplified spontaneous emission (ASE) regime, and it combines several advantages from both LD and LED, such as droop-free, speckle-free, low-spatial coherence, broader emission, high-optical power, and directional beam. Here, SLDs were fabricated by a focused ion beam by tilting the front facet of the waveguide to suppress the lasing mode. They showed a high-power of 474 mW on c-plane GaN-substrate with a large spectral bandwidth of 6.5 nm at an optical power of 105 mW. To generate SLD-based white light, a YAG-phosphor-plate was integrated, and a CRI of 85.1 and CCT of 3392 K were measured. For the VLC link, SLD showed record high-data rates of 1.45 Gbps and 3.4 Gbps by OOK and DMT modulation schemes, respectively. Additionally, a widely single- and dual-wavelength tunability were designed using SLD-based external cavity (SLD-EC) configuration for a tunable blue laser source.

Visible Light Communications Technology and Machine Learning - 2020-10-20

Paul Anthony Haigh, Newcastle University, United Kingdom and Dr. Bo Tan, Tampere University, Finland

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KAUST

In this webinar we will give an overview of several key technologies related to our special issues in Frontiers of Communications and Networks; (1) visible light communications (VLC) and (2) applications of machine learning in optical and wireless communication systems.

KAUST Ph.D student, Jorge A. Holguin-Lerma, awarded 2019 SPIE Optics and Photonics Education Scholarship

1 min read ·
SPIE, the international society for optics and photonics, recently awarded KAUST Ph.D. student Jorge Holguín-Lerma a 2019 Optics and Photonics Education Scholarship for his potential research contributions to optics, photonics or other related fields. Holguín-Lerma joined KAUST in August 2016 and is a member of Professor Boon S. Ooi's Photonics Laboratory.
Salah

Salah Abdeljabar

When it comes to Communication Systems and Networks, there are always newer technologies to explore and many mysteries to solve. Indeed, it is an interesting field to pursue with promising research opportunities. KAUST combines outstanding professors and elite students on one hand, well-established labs and facilities on the other hand, which made it an excellent research environment.

Mahmoud Wafik Eltokhey

Postdoctoral Research Fellow, Electrical and Computer Engineering
Mahmoud Wafik Eltokhey is a Postdoctoral Fellow under the supervision of Professor Mohamed-Slim Alouini at the Communication Theory Lab (CTL) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Mahmoud Wafik Eltokhey received his B.Sc. and M.Sc. degrees from the Department of Electronics and Communications Engineering, Mansoura University, Egypt, in 2012 and 2016, respectively. He received his Ph.D. degree in Optical Communications from École Centrale Marseille, France, in 2021. He was a Research Assistant with the Center for Photonics and Smart Materials

Jia Ye

Jia Ye is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jia Ye obtained her bachelor degree in communication engineering from Southwest University, Chongqing, China, in 2018. Research Interest Jia Ye is focusing in the area of wireless communication. She’s currently working on system performance evaluation of wireless communication systems. Selected Publications J. Ye, G. Pan and M. -S. Alouini, " Earth Rotation-Aware Non-Stationary

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory

Fadil Habibi Danufane

Postdoctoral Research Fellow, Electrical and Computer Engineering
Fadil Habibi Danufane is a Postdoctoral Fellow at the Communication Theory Lab (CTL) of King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Fadil Habibi Danufane received his B.Sc. degree in electrical engineering from Bandung Institute of Technology (ITB), Indonesia, in 2013, and his M.Sc. degree in computational science from Kanazawa University, Japan, in 2017. In 2021, he obtained his Ph.D. degree in telecommunications with the Laboratory of Signals and Systems in CentraleSupélec, Paris-Saclay

Camellia Saeed Mouhammad

I found in KAUST the optimum lifestyle customized for me. Where I can work with world-class professors, have access to leading research labs, also a safe and fun community to live and grow.

Amr M. Abdelhady

Postdoctoral Research Fellow, Communication and Computing Systems Lab
Amr is a Postdoctoral Fellow in the Communication & Computing Systems Laboratory (CCSL) supervised by Professor Ahmed M. Eltawil at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amr earned his bachelor degree in communications and computer engineering from Cairo University Faculty of Engineering (CUFE) in 2012. He worked in Cairo University for Wireless Studies (CWS) center after graduation. He joined King Abdullah University of Science and Technology in 2014 and earned his Master degree in 2016 and his PhD in 2021. Research Interest Amr ‘s research is

Abdullah A. Alatawi

Abdullah Alatawi is a Ph.D. student at Photonics Laboratory at King Abdullah University of Science and Technology (KAUST). His research interests include the study of semiconductor lasers, LEDs, and SLDs for applications in energy-efficient lighting and visible light communications. He received his master of science in optical nanotechnology engineering from the University of Kassel in Germany (2014). The topic of his thesis covers the investigation and improvement of VCSEL devices. He received his B.SC in Aerospace Engineering from King Fahad University of Petroleum and Minerals (KFUPM) in
<> Applied Partial Differential Equations (12)

On Nonlocal Keller-Segel Type Equations

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B1 L3 R3119

In the first part of the talk we investigate a Keller-Segel model with quorum sensing and a fractional diffusion operator. This model describes the collective cell movement due to chemical sensing with flux limitation for high cell densities and with anomalous media represented by a nonlinear, degenerate fractional diffusion operator. The purpose here is to introduce and prove the existence of a properly defined entropy solution. In the second part of the talk we will analyze an equation that is gradient flow of a functional related to Hardy-Littlewood-Sobolev inequality in whole Euclidean space of higher dimensions.

IVth KAUST MaS Workshop on Modelling and Simulation

speakers from KAUST, Birmingham, Graz, Utrecht, Stuttgart, Frankfurt, Buffalo, Linz, Weissach, Lugano, Kaliningrad, Heidelberg, State College, Philadelphia, Torino, Riyadh

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B3 L5 R5209

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Models based on partial differential equations Novel models in biosciences Computational methods in empirical sciences. Numerical methods for partial differential equations Historical and scientific background Previous KAUST MaS workshops were held in 2017, 2018 and 2019 To download the Workshop's Agenda click here. Agenda Monday, 21 March 2022 Day 1 Saudi Arabia(AST) Speakers Description 10:00 Tony Chan (KAUST President) Opening Remarks 10:15 Gundolf Haase

5th KAUST CEMSE MaS Workshop on Modelling and Simulation

Speakers from KAUST, Melbourne, Utrecht, Karlsruhe, Erlangen, Brisbane, Langensteinbach, Lugano, Frankfurt, Italy

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B3 L5 R5209

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Simulating unsteady CO 2 flow through brine-saturated cross-bedded sandstones: towards relative permeability curves for unstable displacement On Fourier methods and machine learning techniques in Computational Finance Pore-scale modelling of flow and transport in porous media Uncertainty Quantification of Density-Driven Groundwater Flow Simulation of unsaturated Density-Driven flow with a phreatic surface Modeling and Large-scale Simulation of Carbon Dioxide

Scalable parallel solvers for cardiac reaction-diffusion models and applications

Luca F. Pavarino, Professor, Department of Mathematics, Università degli Studi di Pavia

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B2 L5 R5220

After a brief introduction to the field of Computational Cardiology and cardiac reentry, we introduce and study some scalable domain decomposition preconditioners for cardiac reaction-diffusion models, discretized with splitting semi-implicit techniques in time and isoparametric finite elements in space.

Professor Peter Markowich Has Been Awarded The "John Von Neumann Visiting Chair" At The Technical University Of Munich

1 min read ·
Professor Peter Markowich has been awarded the "John Von Neumann Visiting Chair" at the Technical University of Munich. The John von Neumann Visiting Professorship allows outstanding scientists to spend a semester at the TUM Center for Mathematics.

Peter Markowich elected Fellow of the European Academy of Sciences

1 min read ·
KAUST Professor Peter Markowich has been elected as a Fellow of the European Academy of Sciences (EurASc). Established in 2003, the EurASC is a fully independent international organization of distinguished scholars that aims to recognize and elect to its membership the best European researchers to strengthen European science and scientific cooperation.

Professor Markowich named Fellow of the Institute of Mathematics and its Applications

1 min read ·
Prof. Peter Markowich has been named a Fellow of the Institute of Mathematics and its Applications (IMA). IMA Fellowships are awarded to individuals who have shown demonstrable achievement and competence in the development or application of mathematics.

Professor Athanasios Tzavaras elected Fellow of the European Academy of Sciences (EurASc)

1 min read ·
Athanasios (Thanos) Tzavaras has been elected Fellow of the European Academy of Sciences (EurASc). He was nominated for his contributions to the interface of nonlinear partial differential equations and applied mathematics in the physical sciences.

Professor Markowich elected to the European Academy of Sciences and Arts

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.

Peter Markowich

Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Sciences
A distinguished professor of applied mathematics and computational science, Dr. Markowich is the author of over 200 publications in leading international journals and circa 14,000 citations.

Julian David Gutierrez Pineda

Julian received both B.S. in Mathematics and B.S. in Engineering from Escuela Colombiana de Ingenieria, Colombia. After receiving his M.Sc. in Mathematical Sciences from Universidad Nacional Autónoma de México, México, he joined Professor Diogo Gomes as a Ph.D. student in applied mathematics at King Abdullah University of Science and Technology, Saudi Arabia. His research interests are primarily optimal control, optimal transport, calculus of variations, partial differential equations, and mean-field games.

Daria Bolbot

Short Bio Ms. Daria Bolbot is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. She graduated from Moscow Institute of Physics and Technology in 2013 with the Master degree in Applied Mathematics and Applied Physics. Ms. Daria got a Bachelor degree from the same institute (MIPT) 2 years earlier - 2011. Her research interests are Nonlinear Partial Differential Equations, Hyperbolic Conservation Laws, Dispersive-Diffusive Shock Waves and Dynamical
<> biomedicine (12)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Causality-based new drug development: Some successful cases and a new challenge

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B2 L5 R5209

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

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B1 B4 B4214

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

Accelerating biomedical discovery with an Internet of FAIR data and services

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B9 L2 H1 R2322

In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.

Towards Accurate Biomedical Genomics Anywhere Anytime - Graduate Seminar

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B9 L2 H1 R2322

Abstract Current genetic diagnosis by next-generation sequencing requires a large investment of resources and offers little point-of-care portability. Furthermore, it is unable to detect many types of genetic variations - including large deletions, duplications, and balanced translocations - that are relevant to human diseases and health. Comparing to other sequencing technologies, Nanopore sequencing owns the advantages of point-of-care (i.e., sequencing anywhere anytime), long reads (i.e., assembly-free to detect various genetic variations), and PCR free (i.e., sample preparation is easy)

Targeting cancer cells with tiny magnetic wires

1 min read ·
Magnetic nanowires with weak magnetic fields and low frequencies can destroy cancer cells without generating heat.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

CEMSE Master Student Mayyada Hawsawi brings KAUST research to Falling Walls 2017

1 min read ·
Mayyada Hawsawi, master student in the Sensing, Magnetism, and Microsystems Research Group, directed by Professor Jurgen Kosel, brought a piece of KAUST's research to the Falling Walls Global Lab Finale on November 8th 2017, in Berlin, Germany.

Mayyada Hawsawi

​Mayyada Hawsawi obtained a Master's degree in Sensing, Magnetism and Microsystems (SMM) research group under the supervision of Prof. Jurgen Kosel at KAUST. Education and Early Career Mayyada has a Bachelor’s Degree in Biomedical Engineering from Boston University in 2016. She received her Master's Degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) in 2017. Research Interest Mayyada's research interests include Magnetic sensors, Sensor fabrication, and Biomedicine. Education Profile M.Sc. Electrical Engineering, King Abdullah University of Science
<> cyber-physical systems (12)

Advancing Security Red-Teaming in Cyber-Physical Systems via AI-Driven Side-Channel Analysis

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B9 L3 R3223

This talk will systematically reveal security and privacy vulnerabilities in cyber-physical systems by characterizing contactless side-channel attacks on various components and proposing effective defense methods using hardware-software co-design and AI-driven techniques, while also outlining future research directions for developing secure and privacy-preserving CPS platforms.

Traffic Monitoring and MAC-Layer Design for Future IoT Systems

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B1 L4 R4214

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

Cross-Layer Security of Embedded and Cyber-Physical Systems

Prof. Mohammad Alfaruque, University of California, Irvine, USA.

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B2 5220

Abstract Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components . Embedded systems comprising of hardware and software systems are the major enabling technology for these cyber-physical systems. Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information processing systems, due to the tight interactions between cyber and physical components

Model-based Design of Cyber-physical Systems with Learning Components

Prof. Panagiotis Katsaros, Aristotle University of Thessaloniki

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B2 5220

Abstract Cyber-physical system design involves heterogeneous components for sensing, control, actuation, networking and relies on models for the system’s physical dynamics, and all interactions with its environment. Since safety and security are critical, model-based engineering methods are often used to increase the confidence and guide the validation testing of the system under design. An abundant of results from modern system theory (discrete, continuous and hybrid) provides the foundations for modeling, simulation, testing and formal verification. Mature tools and languages offer advanced

Resilient Cyber-Physical and Embedded Systems - Or how to cheat to get to n=f+1

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B5 L5 R5209

Our society keeps entrusting ICT systems with high value cyber-only assets, such as our most sensitive data, finances, etc. However, when it comes to cyber-physical systems and their ability to act in and with the physical world, lifes are at risk and require rigorous protection against accidental faults and cyberattacks.

Securing Cyber-Physical and IoT Systems in Smart Living Environments

Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.

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B4 L5 R5220

Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.

Meet KAUST new student: Ioannis Zografopoulos

1 min read ·
Ioannis Zografopoulos is a Ph.D. student who joined KAUST this fall as a Ph.D. candidate and member of the Secure Next Generation Resilient Systems (SENTRY) Lab under the supervision of Professor Charalambos Konstantinou. Zografopoulos believes that KAUST's renowned faculty, global reputation and research infrastructure make the University the ideal environment for him to develop high-quality, impactful research.

Ioannis (Yannis) Zografopoulos

Ioannis Zografopoulos received his Ph.D. at the Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE) of King Abdullah University of Science and Technology (KAUST) in 2023. Prior, he graduated with a B.Eng. and M.Eng. degrees in Computer, Communications, and Network Engineering, and an M.Sc. degree in Electrical and Computer Engineering from the University of Thessaly, Volos, Greece, in 2014 and 2015, respectively. His research interests include cyber-physical systems security, with an emphasis on embedded systems for industrial, distributed energy, and power grid

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Enas M. Odat

Enas Odat received the B.S. degree in computer engineering from the Jordan University of Science and Technology in 2009, and the M.S. degree from the Department of Computer Science, King Abdullah University of Science and Technology, in 2011, where she is currently pursuing the Ph.D. degree. Her research interests include machine learning, game theory, and Cyber-Physical systems. Education Profile MS. Computer Sciences, King Abdullah University of Sciences and Technology (KAUST), 2011 BS. Computer Engineering, Jordan University of Science and Technology (JUST), 2009

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Bodhibrata Mukhopadhyay

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bodhibrata Mukhopadhyay is a Postdoctoral Fellow at Computer Electrical and Mathematical Sciences and Engineering Center (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in Communication Theory Lab (CTL). Education and Early Career Bodhibrata Mukhopadhyay received his Master of Science (MS(R)) degree in Telecommunication and PhD degree in Electrical Engineering from the Indian Institute of Technology Delhi (IITD), India, in 2015 and 2021. Since 2017, he has been the Director of Suxma Systems (an IIT Delhi incubated startup), which