Topics

<> evolutionary biology (2) <> exascale (2) <> explainable AI framework (2) <> extreme environmental events (2) <> fabrication (2) <> Faculty Support (2) <> fault tolerance (2) <> Fault-Tolerant Computing (2) <> FEMSimulation (2) <> Financial Reports (2) <> finite element method (2) <> flexible nanodevices (2)

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.

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
<> flexible solar cells (2)

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.

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
<> flood monitoring (2) <> Fluid Mechanics (2) <> FMCW radar (2) <> Free-space optical wireless communication (2) <> functional prediction (2)

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

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
<> Functional Time Series (2) <> Fusion (2) <> Future (2)

Towards Robust and Accurate Navigation: Theory, Applications, and Future Directions

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KAUST

Navigation is an essential requirement for many applications (commercial, retail, military, scientific, ...etc) and in a variety of environments (in-doors, outdoors, space, underwater, and even underground). In this talk, I will overview some of my group's work in localization and navigation focusing on indoor and satellite positioning. The talk will demonstrate how the structure or constraints of the problem can help achieve very accurate localization (e.g. millimeter level indoors) that is robust to Doppler, multipath, and shadowing. The talk will also touch upon various related applications that the group is pursuing in smart health and smart cities. The talk will end with future directions for localization in extreme environments and in the TeraHertz spectrum where localization, environment sensing, and communication converge.

Distributed Multi-Antenna Communications with Radio Stripes

Prof. Emil Björnson, KTH Royal Institute of Technology, Stockholm, Sweden

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KAUST

In this talk, we will consider an alternative approach for future networks: serve the users by antennas that are distributed over the coverage area. By shifting from a world where base stations are surrounded by users, to a world where each user is surrounded by antennas, we can deliver almost uniformly good data rates wherever the user is. This concept has recently been called Cell-Free Massive MIMO. A core practical challenge is to deploy such a distributed MIMO array affordably. One potential approach is to use radio stripes, which are cables with integrated antennas. We will take a close look at how these can be implemented and recent results on how communications algorithms can be designed to exploit their special characteristics.
<> Future Mobility (2) <> GaN-based electronics (2) <> gene-disease associations (2) <> genetic variation (2)

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

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
<> geology (2) <> geometric diodes (2) <> global connectivity (2) <> gold nanosphere (2) <> Gradient flows (2)

Derivative-Free Global Minimization: Relaxation, Monte Carlo and Sampling

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B9 L2 H2 H2

We develop a derivative-free global minimization algorithm that is based on a gradient flow of a relaxed functional. We combine relaxation ideas, Monte Carlo methods, and resampling techniques with advanced error estimates. Compared with well-established algorithms, the proposed algorithm has a high success rate in a broad class of functions, including convex, non-convex, and non-smooth functions, while keeping the number of evaluations of the objective function small.

Simone Portaro

Simone Portaro is a Ph.D. Student in the Computer, Electrical, Mathematical Sciences and Engineering (CEMSE) department at the Applied Mathematics and Computational Sciences (AMCS) program under the supervision of Professor Peter Markowich at King Abdullah University of Science and Technology. Simone received his B.S. degree in Industrial Engineering from the University of Catania, Italy in 2017. In 2020, he received his M.S. degree in Mathematical Engineering from Politecnico di Torino, Italy. Research interests Nonlinear Partial Differential Equations, Analysis-Modeling of biological network
<> graphical models (2) <> Hackathon (2) <> Hardware (2) <> Hardware acceleration (2) <> hardware security (2) <> health monitoring (2) <> healthcare technologies (2) <> hierarchical matrices (2) <> High Performance Low Power Circuits (2)

Kuilian Yang

Kuilian Yang is a Former visiting student at the Integrated Circuit and System Research Group under the supervision of Prof. Hossein Fariborzi at King Abdullah University of Science and Technology (KAUST). He is currently working on the development of high performance low power circuit and low noise readout circuit for sensors. Professional Memberships ​University of Electronic Science and Technology of China (UESTC)​​​​.

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.
<> History Matching (2) <> Human Activity Recognition (2) <> human bioinformatics (2) <> Human-robot interaction (2)

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

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
<> human-robot interactions (2)

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

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
<> HVDC (2)

A Novel HVDC Architecture for Offshore Wind Farm Applications

Otavio Dezem Bertozzi Junior, MS Student, Physical Science and Engineering

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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 thesis, we introduce a novel power converter solution for the parallel connection of high-power offshore wind turbines, suitable for HVDC collection and transmission. For the parallel operation of energy sources in an HVDC grid, DC link voltage control is required. The proposed system is based on a full-power rated uncontrolled diode bridge rectifier in series with a partially-rated fully-controlled thyristor bridge rectifier. The thyristor bridge acts as a voltage regulator to ensure the flow of the desired current through each branch, where a reactor is placed in series for filtering of the DC current. AC filters are installed on the machine side to mitigate harmonic content. The mathematical modeling of the system is derived and the control design procedure is discussed. Guidelines for equipment and device specifications are presented. The concept is validated through simulation, and an experimental framework for testing of the system is suggested.
kaust-cemse-ece-otavio-bertozzi

Otavio Bertozzi

<> ICVSS (2) <> IEEE award (2) <> III - V compound semiconductors (2) <> III-nitride (2) <> III-Nitride Optoelectronics (2) <> image (2)

Research at the Image and Video Understanding Lab (IVUL) - Graduate Seminar - ECE

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KAUST

In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding): We aim to extract meaningful semantic information from large-scale video data by tackling research problems such as object tracking, activity detection, moment retrieval, and language grounding in video. Theme 2 (Visual Computing for Automated Navigation): We develop methodology to enable more accurate, reliable, and robust perception of the visual world for automated navigation applications (e.g. self-driving cars and UAVs). In this theme, we tackle research problems such as object tracking, segmentation, and detection in 3D data, as well as transfer learning from simulation (sim2real). Theme 3 (Fundamentals/Foundations): In this theme, we tackle fundamental research problems in CV and ML that transcend specific applications with focus on deep network theory/analysis (e.g. robustness, certification, and interpretability) and structured optimization methods for large-scale CV/ML problems. Throughout the talk, I will highlight some of the interesting projects at IVUL to encourage students to get interested in the research field.

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.
<> image synthesis (2) <> in-memory sensing (2) <> indoor localization systems (2) <> infoclouds (2) <> Information retrieval (2) <> integral equations (2) <> Integrated Sensors (2) <> Intelligent Communication (2) <> interactive visualization (2) <> interference management (2) <> Internet of Bodies (2) <> invariance (2)

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
<> Inverse problem (2) <> IoT devices (2) <> IVUL (2)

Research at the Image and Video Understanding Lab (IVUL) - Graduate Seminar - ECE

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KAUST

In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding): We aim to extract meaningful semantic information from large-scale video data by tackling research problems such as object tracking, activity detection, moment retrieval, and language grounding in video. Theme 2 (Visual Computing for Automated Navigation): We develop methodology to enable more accurate, reliable, and robust perception of the visual world for automated navigation applications (e.g. self-driving cars and UAVs). In this theme, we tackle research problems such as object tracking, segmentation, and detection in 3D data, as well as transfer learning from simulation (sim2real). Theme 3 (Fundamentals/Foundations): In this theme, we tackle fundamental research problems in CV and ML that transcend specific applications with focus on deep network theory/analysis (e.g. robustness, certification, and interpretability) and structured optimization methods for large-scale CV/ML problems. Throughout the talk, I will highlight some of the interesting projects at IVUL to encourage students to get interested in the research field.

Fabian participates in the Computer Vision Summer School

1 min read ·
An IVUL Ph.D. student (Fabian Caba)​ participates in the Computer Vision Summer School.
<> KACARE (2) <> Kinetic Theory (2) <> knowledge graphs (2)

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

Towards a Universal Knowledge Graph Chatbot: ChatGPT versus Traditional Question Answering for Knowledge Graphs

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B9 L2 H2 H2

Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information efficiently and effectively. While Conversational AI simulates human-like conversations, its effectiveness is limited by the available training data. However, QASs retrieve the most up-to-date information from KGs by translating natural language queries into formal queries that the database engine can process. In this talk, we examine the characteristics of existing approaches for combining Conversational AI and QASs to create novel KG chatbots. We also introduce KGQAn, a universal QA system that can be applied to any KG without the need for customization.
<> landslide susceptibility modeling (2)

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.

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
<> large languaje models (2) <> latent Gaussian models (2) <> Latent variable models (2) <> learning (2) <> LED Wireless Communication Systems (2) <> Linear (2) <> linear systems (2)

On Optimal Regularization in Estimation, Detection, and Classification

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KAUST

In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.

Robust fractional-order proportional-integral observer for synchronization of chaotic fractional-order systems

1 min read ·
Ibrahima N' Doye, et al., "Robust fractional-order proportional-integral observer for synchronization of chaotic fractional-order systems", IEEE/CAA Journal of Automatica Sinica 6 (1), 2019, 268. Abstract: In this paper, we propose a robust fractional-order proportional-integral (FOPI) observer for the synchronization of nonlinear fractional-order chaotic systems. The convergence of the observer is proved, and sufficient conditions are derived in terms of linear matrix inequalities (LMIs) approach by using an indirect Lyapunov method. The proposed FOPI observer is robust against Lipschitz
<> living systems (2) <> Low complexity sphere decoding (2) <> LSG (2) <> Lyapunov Theory (2)

Exact Backstepping Control for Systems in Pure Feedback Form

Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany

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KAUST

Traditional backstepping approaches may struggle to asymptotically stabilize systems in pure feedback form, due to its inherent implicit equations. Approximation based designs only have a limited domain of validity and turn out sensitive to model uncertainty and disturbances. We propose a new design that circumvents the necessity of solving implicit algebraic equations by introducing new state variables. Additional augmentations to the backstepping Lyapunov design lead to explicit expressions for the associated differential equations. The result is a dynamic state feedback, capable of asymptotically stabilizing the origin of a general class of nonlinear systems, based on just standard assumptions.

Part 2 - Introduction to backstepping control

Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany.

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KAUST

Backstepping is a widely applicable control technique based on Lyapunov theory that under rather mild assumptions leads to families of control laws for a large class of nonlinear systems. Focusing on systems of ordinary differential equations, we introduce the basic concept (integrator backstepping), generalize it, among others, to systems in strict feedback form and pure feedback form, which all enjoy an inherent controllability property, captured in the system structure. The course ends with extending the setting to the adaptive backstepping case, resorting to the certainty equivalence principle and Barbalat's lemma. The course is furnished by a series of exercises to let the students gather experience on tailored examples.
<> machine intelligence (2) <> Machine Learning Systems (2) <> macroeconomics (2) <> magnetic nanowires (2) <> Marine Robotics (2) <> marketing (2) <> markov chains (2) <> Mathematical Analysis (2) <> Mathematical Finance (2) <> mathematical statistics (2) <> mechanical (2) <> Mechanics (2)

On some nonlinear elliptic/parabolic systems arising in mechanics and turbulence

Prof. Luigi Carlo Berselli

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B2 L5 R5220

I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00

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
<> mechatronic systems (2) <> medicine (2) <> memory devices (2) <> Meta Learning (2) <> Metagenomics (2)

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.

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.
<> Methods (2) <> microbiology (2)