machine learning

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).

Ebrahim Al-Safadi

Intern, Information Science Lab 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).

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

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.

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

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

Meet KAUST prospective student: Sahar Ammar

1 min read ·
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 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.

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.''

Broadening horizons for people with quadriplegia

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

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.

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

1 min read ·
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.

Giving voice to the voiceless through assistive technology

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

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.

Xiangyu Cui

Xiangyu Cui is a Master student at Communication Theory Laboratory(CTL), King Abdullah University of Science and Technology (KAUST), working with Professor. Mohamed-Slim Alouini in CTL. Education and Early Career Xiangyu Cui studied Communication Engineering and got his Bachelor of Engineering from University of Electronic Science and Technology of China in 2023. Honors and Awards Xiangyu Cui was awarded National scholarship in China in both 2020 and 2021. Education Profile B.S. in Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China (September

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.

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)

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.

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)

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

Raphael Bridi

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

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.

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 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.

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

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

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..

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

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).

Mamdouh AlJoud

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

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

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.

Khalil Elkhalil

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

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).

Juan Manuel Marin Mosquera

PhD Student juan.mosquera@kaust.edu.sa Introduction Juan M. Marin is a PhD student in Electrical Engineering at King Abdullah University of Science and Technology (KAUST). He received a BSc in Electrical Engineering from The National University of Colombia in 2021. His research focuses on Distributed Fiber Optic Sensing (DFOS) and developing multi-configuration systems based on optical fiber, embedding DFOS in optical communication and power delivery networks. Research Interests Fiber Optics Distributed Fiber Optic Sensing Machine Learning Selected Publications Yujian Guo*, Juan M. Marin*

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

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.

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

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

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

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

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.

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

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. Educ ation 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

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

Ebrahim Safadi

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

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

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.

Basit Olakunle Alawode

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

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).

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

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

Ahmed Foud Ali Mehdi AlMehdhar

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

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

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)

Abdulrahman Aljedaibi

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

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 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)