Deep learning

Aznaur Aliev

M.S. Student

Elham Binshaflout

Elham is a PhD Student in Electrical and Computer Engineering program (ECE) at KAUST. Focuses on smart cities solutions using deep learning.

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

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.

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.

Learning for Visual Data Synthesis and Analysis

Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany

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B9 L2 H1 R2322

Abstract In this talk, I will present our recent advances in deep learning to synthesize and analyze visual data, such as images, volumes, and point clouds. The presented approaches are loosely aligned along the classical computer graphics rendering pipeline, whereby both structured and unstructured data are handled. I will first present concepts for learning in object space, i.e., directly on the data to be rendered. To realize different visual tasks, such as normal estimation and segmentation, I will discuss how Monte Carlo integration can be used to realize convolutions on point cloud data

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.

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

EE+CS Graduate Seminar: Deep Graph Convolutional Networks

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KAUST

In this talk, I will present a line of work done at the Image and Video Understanding Lab (IVUL), which focuses on developing deep graph convolutional networks (DeepGCNs). A GCN is a deep learning network that processes generic graph inputs, thus extending the impact of deep learning to irregular grid data including 3D point clouds and meshes, social graphs, protein interaction graphs, etc. By adapting architectural operations from the CNN realm and reformulating them for graphs, we were the first to show that GCNs can go as deep as CNNs. Developing such a high capacity deep learning platform for generic graphs opens up many opportunities for exciting research, which spans applications in the field of computer vision and beyond, architecture design, and theory. In this talk, I will showcase some of the GCN research done at IVUL and highlight some interesting research questions for future work.

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.

Deep Continual Learning

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KAUST

In this seminar, I will present some of the work I have done on Continual Deep Learning, among the research topics at the Vision-CAIR group. Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity as adopted in modern deep learning techniques. Decreasing the gap towards human-level continual learning, we extended continual deep learning from multiple perspectives. The Hebb's learning theory from biology can be famously summarized as “Cells that fire together wire together.". Inspired by this theory from biology, we proposed Memory Aware Synapses (ECCV18) to quantify and reduce machine forgetting in a way that enables leveraging unlabeled data, which was not possible in former techniques. We later developed a Bayesian approach appearing at ICLR2020, where we explicitly modeled uncertainty parameters to orchestrates forgetting in continual learning. We showed in our ICLR2019 and ACCV18 works that task descriptors/ language can operate in continual learning visual tasks to improve learning efficiency and enable zero-shot task transfer. Beyond computer vision tasks, we recently developed an approach appearing at ICLR2020 we call "Compositional Language Continual Learning". We showed that disentangling syntax from semantics enables better compositional Seq2Seq learning and can significantly alleviate forgetting of tasks like machine translation. In the talk, I will go over these techniques and shed some light on future research possibilities.

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.

Compressed communication in Distributed Deep Learning - A Systems perspective

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KAUST

Network communication is a major bottleneck in large-scale distributed deep learning. To minimize the problem, many compressed communication schemes, in the form of quantization or sparsification, have been proposed. We investigate them from the Computer Systems perspective, under real-life deployments. We identify discrepancies between the theoretical proposals and the actual implementations, and analyze the impact on convergence.

Overcoming the curse of dimensionality: from nonlinear Monte Carlo to deep learning

Arnulf Jentzen, Professor, Applied Mathematics Münster: Institute for Analysis and Numerics, University of Münster

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KAUST

In this talk we prove that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and we thereby prove, for the first time, that a general semilinear parabolic PDE can be solved approximatively without the curse of dimensionality.

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.

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.

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.

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.

On training algorithms for neural networks

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B9 L2 R2322 H1

In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms

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.

Extraction of Complex DNN Models: Real Threat or Boogeyman? - Graduate Seminar

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B9 L2 H2 H2

The success of deep learning in many application domains has been nothing short of dramatic. The success has brought the spotlight onto security and privacy concerns with deep learning. One of them is the threat of "model extraction": when a machine learning model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model. In this talk, I will describe our work in exploring whether model extraction constitutes a realistic threat. I will also discuss possible countermeasures, focussing on deterrence mechanisms that allow for the verification of ownership of ML models.

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.

Averaging, Momentum, and Schedulers in Optimization for Deep Learning

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B9 L3 R3128

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.

Optimization for Deep Learning

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B5 L5 R5209

Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these

Fabian and Victor participate in the annual MSR Summer School!

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Two IVUL Ph.D. students (Fabian Caba and Victor Escorcia) participate in the annual MSR Summer School.

Somayah Albaradei wins at City of the Future competition

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​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.

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.

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.

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: 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).

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.

Dr. Harrou receives two IEEE ECBIOS 2021 best paper awards

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Dr. Fouzi Harrou, a research scientist in the KAUST Environmental Statistics (ES) group, recently received two IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2021 (IEEE ECBIOS 2021) best paper awards.

Meet KAUST new student: Qilong Pan

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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.

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.

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
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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.

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

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).

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).

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

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

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

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

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan is a MS/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 life and study at KAUST would be an unforgettable experience for his whole life. Education and early career B.S. in Statistics in 2021, Wuhan University of Technolgy B.A. in English in 2021, Huazhong University of Science and Technology Research Interests Environmental statistics, Spatio-temporal statistics, Deep Learning

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.

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

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

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.

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

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

Mohammed Aljahdali

M.S. Student, Computer Science
Mohammed Khalid Aljahdali is a M.S./Ph.D. student in computer science program and member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab under the supervision of Professor Marco Canini. Research Interests Mohammed's research interests include deep learning.

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

Maurizio Filippone

Associate Professor, Statistics
KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics, namely, enabling sound decision-making through uncertainty quantification in model parameters and predictions. His main research interests are in models based on deep learning and Gaussian processes

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

Matthew Sainsbury-Dale

Consultant, Statistics
Matthew Sainsbury-Dale is a PhD student at the University of Wollongong (UOW), and has already submitted his PhD. In 2022, Matthew spent 5 months visiting the Extreme Statistics Research Group at KAUST. Furthermore, from March to August 2023, he worked for KAUST in the extSTAT research group as a remote consultant. Since January 2024, while waiting for feedback from his PhD committee (and before relocating as a postdoc at KAUST), he is again working in the extSTAT group as a remote consultant. Education and Early Career Matthew Sainsbury-Dale obtained a Bachelor of Mathematics Advanced

Malik Shahid Sultan

Ph.D. Student, Statistics
Malik Shahid Sultan is a Ph.D. student in Bio-Engineering ( Bioinformatics & Machine Learning) at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group. Education and Early Career Malik Shahid Sultan hold BSc. in Industrial Engineering with a Gold Medal from the University of Engineering and Technology Taxila (Pakistan). Malik holds M.Sc. from King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) in Bio-Engineering (Bioinformatics & Machine Learning). During the Fall of 2021

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

Lucas Câmara Dantas Bezerra

PhD candidate working on cooperation in multi-agent systems through Multi-Agent Reinforcement Learning (MARL). He is affiliated with the Distributed Systems and Autonomy (DSA) group, under supervision of Professor Shinkyu Park.

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

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

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

Juan C. Pérez

Ph.D. Student, Computer Science
Juan C. Pérez is 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.

Juexiao Zhou

Ph.D. Student, Computer Science
Juexiao Zhou is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Juexiao majored in Bioinformatics and graduated from the Southern University of Science and Technology (SUSTech), located in Shenzhen, Guangdong, China. Research Interests IngIntelligent Healthcare: develop machine learning (ML) / deep learning (DL) techniques for healthcare, including disease detection, risk assessment, etc. Bioinformatics: develop new methods for bioinformatics tasks, including gene regulation understanding

Jinane Bazzi

Jinane Bazzi is an MS/PhD student at KAUST, focusing on developing compute-in-memory architectures for efficient neural network computations, driven by a passion for advancing AI hardware.

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 is 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

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

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

Hasan Hasan

Hasan Hasan 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 and

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

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

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

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

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

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

Elaf Jameel Islam

Research Interests Evaluation of deep learning methods in recognition of genomic signals. Academic Advisor Prof. Vladimir Bajic. Education ​B.Sc., Computer science, UQU, MAKKAH, Saudi Arabia, 2014 ​2015-2018: Master student, KAUST, Thuwal, Saudi Arabia

Diego Augusto Silva

Diego Augusto Silva is a Ph.D. candidate in the electrical and computer engineering department and a member of the Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil at King Abdullah University of Science and Technology (KAUST). Research Interests Diego's research interests include the digital logic design of algorithms and event-based cameras. Education Profile Bachelor's degree in electrical engineering from the Federal University of São João del-Rei, Brazil. Master's degree in electronic engineering and computing in electronic devices and systems from

Duc Minh Nguyen

Duc Minh Nguyen is a Ph.D. student under the supervision of Professor 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 Minh is a graduate from Dankook University, Republic of Korea (2018) where he received a B.Eng. Degree in Mobile Systems Engineering, with a minor in International Business Administration. During his time at Dankook University, Minh worked on Social Networks Analysis. He developed an accurate spatial clustering algorithm based on

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.

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

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

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

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

Azimkhon Ostonov

Ph.D. Student, Computer Science
Azimkhon Ostonov is a PhD candidate at King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Mikhail Moshkov in Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES). Education and Early Career Azimkhon Ostonov studied at the National University of Uzbekistan in Uzbekistan. He obtained his bachelor's degree in Applied Mathematics and Informatics in 2012 and his master's degree in Computer Systems and their Software in 2014. Azimkhon started his career as a junior programmer at Fido-Biznes in Tashkent. Then, he

Asmaa Abdallah

Postdoctoral Research Fellow, Electrical and Computer Engineering
Asmaa Abdallah r eceived the B.S. (with High Distinction) and M.S degree in computer and communications engineering from Rafik Hariri University (RHU), Lebanon, in 2013 and 2015, respectively. In 2020, she received the Ph.D. degree in electrical and computer engineering at the American University of Beirut (AUB), Beirut, Lebanon. Ms. Abdallah was the recipient of the Academic Excellence Award at RHU in 2013 for ranking first on the graduating class. She also received a scholarship from the Lebanese National Counsel for Scientific Research (CNRS-L/AUB) to support her doctoral studies. She is

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. Publications dblp Scopus LinkedIn Education Profile M.Sc. in

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

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.

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

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 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

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

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