Profiles

Elham is currently a PhD candidate at the Communications and Computing Systems Laboratory (CCSL). She received M.Sc. degree in Software Systems Engineering from the Department of Computer Science at University College London (UCL), U.K., in 2018. She recieved B.Sc. degree in Computer Science from Imam Abdulrahman University, Alkhobar, KSA, in 2012. 

Elham's research focuses on AI-driven traffic scene understanding at urban intersections, combining advanced sensing technologies with graph representations to downstream intelligent transportation system applications, including real-time traffic monitoring, risk assessment, behavior analysis, flow prediction, and adaptive traffic control. 

Elnur Gasanov is a PhD candidate in the Optimization and Machine Learning Lab at the Center of Excellence for Generative AI (GenAI) at KAUST, where he is advised by Professor Peter Richtárik. His research focuses on distributed machine learning, stochastic optimization, and randomized linear algebra. Elnur holds a Master of Science in Computer Science from KAUST and a Bachelor's degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology.

Optimization and machine learning.

Fahad Aljehani is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where he is supervised by Professors Taous‑Meriem Laleg‑Kirati and Eric Feron. His research integrates classical control theory with modern AI and machine‑learning techniques to create advanced control and estimation frameworks for complex process systems, particularly optimal feeding in aquaculture and bacteria monitoring in wastewater treatment plants.

Aljehani earned his M.S. in Electrical Engineering from KAUST (2019), developing control strategies for distributed solar collectors and a virtual sensor for solar‑irradiance estimation. He holds a B.S. in Electrical Engineering from University of Dayton (2016).

Fahad's research focuses on designing advanced control algorithms and estimation techniques to optimize process control systems, specifically in aquaculture and wastewater treatment. He utilizes a multidisciplinary approach that combines classical control theory with cutting-edge artificial intelligence and machine learning methodologies to develop adaptive, efficient, and scalable models that enhance system performance, improve sustainability, and reduce operational costs. 
Area of interests: 

• Optimal control for a class of nonlinear systems
• Estimation and observer design methods in nonlinear systems 
• Reinforcement learning and dynamics programming
• Computer vision
• Machine learning

Fahad S. Alqurashi is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. His research focuses on advanced wireless communication technologies for connecting underserved and remote regions, emphasizing cost-effective and high-capacity solutions. His work explores Free Space Optics (FSO), TV White Space (TVWS), and hybrid RF/mmWave systems to achieve point-to-point data rates exceeding 10 Gbps, with a particular interest in maritime and rural connectivity.

Fahad holds a Master of Science in Electrical Engineering from KAUST, where his thesis focused on modeling FSO communication channels for next-generation deployment scenarios. He completed his Bachelor of Science in Electrical Engineering (Electronics and Communication track) at Umm Al-Qura University in Makkah, Saudi Arabia.

Fahad has led strategic connectivity projects in collaboration with global technology leaders such as Google Taara, Meta, Cambium, and national operators like Zain and STC. He is currently spearheading national-scale initiatives with the Communication, Space and Technology Commission (CST), Red Sea Global, and Neom to deliver resilient, sustainable, and scalable wireless infrastructure—including projects connecting offshore islands and rural villages using hybrid FSO/RF links powered by solar systems.

His research has been presented at major international venues including IEEE ICC and the Optical Wireless Communication Conference, and he is an active member of IEEE and the Optical Wireless Communication community. Fahad’s interdisciplinary work supports Saudi Vision 2030 and reflects a strong commitment to impactful digital transformation through cutting-edge wireless innovation.

Alongside the Saudi Vision 2030, Fahad’s interest lies in supporting the future of 5G and 6G. Because of this, he focuses on wireless communication systems, especially in free space optical communication (FSO).

Fares Banjar received his B.S. in electrical engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2021 and his M.S. in electrical and computer engineering from King Abdullah University of Science and Technology (KAUST) in 2024. He is currently pursuing a Ph.D. in the Photonics Laboratory at KAUST. He also worked in the industry with Saudi Aramco for two years as an Electrical Engineer.

Power-over-fiber systems and fiber-optic-based sensing and communication networks.

Fares Fourati is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST). His work has been published in leading AI and machine learning venues, including ICML, AAAI, AISTATS, EMNLP, and ECAI.

He has received several distinctions, including consecutive CEMSE Dean’s List Awards, first place in the ACM-SIAM Student Competition (2024), and first place at the Marconi Society Connectivity Summit (2021). Fares earned his Diplôme d’Ingénieur from École Polytechnique de Tunisie in 2020 and an M.S. in Electrical and Computer Engineering from KAUST in 2022.

In addition to his research, he has been actively involved in teaching and mentoring in AI and machine learning at KAUST and across Saudi Arabia. He is also the author of To What End? Meditations on AI and Humanity, reflecting his broader interest in the philosophical implications of AI.

Fares' research focuses on reinforcement learning, deep learning, large language models, and optimization, with an emphasis on advancing the foundations of intelligent systems. 

Faris Alkhalifah is an MS Electrical and Computer Engineering student with the Integrated Telecommunication and Sensing Systems (ITASS) Research Group headed by Professor Abdulrahman Alhamed at King Abdullah University of Science and Technology (KAUST).

Fernando Rodriguez Avellaneda is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Paula Moraga.

Fernando research focuses on highly accurate data analyst adept at collecting, analyzing, and interpreting large datasets, developing new forecasting models, and performing data management tasks. He possesses extensive analytical skills, strong attention to detail, and the ability to work in team environments.

Glen Isaac Maciel García is a Ph.D. Candidate in Applied Physics at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, where he conducted research under the supervision of Prof. Xiaohang Li. He earned his bachelor’s degree in Nanotechnology from the National Autonomous University of Mexico (UNAM). Glen has co-authored 19 peer-reviewed publications, including two first-author papers that were featured as cover articles in Nano Letters and Advanced Electronic Materials.

Glen's research interests include optoelectronics, metamaterials, and nanofabrication. His research focuses on nanoscale devices based on ultra-wide bandgap semiconductors. His work spans device design, fabrication, and characterization, with a particular emphasis on unconventional architectures that extend the limits of traditional semiconductor functionality.

Gokul is an electrical engineering student who is currently doing research in core areas like power electronics, power systems and control systems. His research mainly focuses on grid design, modeling, control and development of modular power converters which helps in creating new solutions for sustainable energy generation, transmission, distribution and consumption.

He is experienced in Hardware-In-the-Loop (HIL) and Rapid Control Prototyping (RCP) systems like RTDS, Opal-RT, Speedgoat, Typhoon and dSPACE. He has great experience in MATLAB Simulink, NI Multisim, OrCAD Pspice and other ECAD tools which include Altium, Eagle and KiCAD.

The project he was involved in with KAUST as a visiting student was the development of a bi-directional multi-node Smart DC Grid for Autonomous Power Interchange Systems (APIS) which includes Energy Storage Systems (ESS). The system is designed in MATLAB Simulink and tested in real-time machines like Speedgoat and Opal-RT which links the software framework called Hyphae by Sony CSL, LF Energy. 

He was involved in other research projects like control design of the 6-ph PMSM motor, Level - 3 Autonomous Vehicle, High Voltage Low Power Converter,  Robust Steering Controller Design, Modelling of the University's Distribution Network etc...

Gokul's research focuses on power electronics, power systems, and control systems, with an emphasis on grid architecture, advanced modeling, control strategies and the development of modular power converters to optimize sustainable energy generation, transmission, distribution, and consumption. He has substantial expertise in Hardware-In-the-Loop (HIL) and Rapid Control Prototyping (RCP) platforms such as RTDS, Opal-RT, Speedgoat, Typhoon, and dSPACE. Skilled in MATLAB Simulink, NI Multisim, OrCAD Pspice, and ECAD tools including Altium, Eagle, and KiCAD.

• King Abdullah University of Science and Technology (KAUST) December 2021 - Present

PhD in Applied Mathematics and Computational Sciences Thuwal, Saudi Arabia, Advisor: Peter Richtarik 

• King Abdullah University of Science and Technology (KAUST) August 2020 - December 2021

MS in Applied Mathematics and Computational Sciences Thuwal, Saudi Arabia
Advisor: Peter Richtarik 

• Moscow Institute of Physics and Technology (MIPT) September 2014 - July 2019

BS in Applied Mathematics and Physics Dolgoprudny, Russia, Advisor: Boris Polyak 
Thesis: Averaged Heavy Ball Method

Stochastic Optimization, Distributed Optimization, Federated Learning, Machine Learning

As an Electrical Engineer, I have extensive knowledge in Power Electronics, Energy Conversion, Power Systems, and Energy Flexibility. Also, I have strong experience and love for Control Theory and Optimization. I am currently studying Machine Learning and its applications.

• Power electronics
• Power systems
• Anomaly detection
• Artificial intelligence

Hanan Alahmadi is a Ph.D. candidate in the Statistics program (CEMSE) at King Abdullah University of Science and Technology (KAUST), specializing in spatial methods for health surveillance in Saudi Arabia, with a focus on data integration and cluster detection under the supervision of Professor Paula Moraga. Her research combines satellite and health data to monitor disease and environmental risks and to develop GIS platforms that support data-driven decision-making. She holds a master’s degree in statistics from KAUST, where she was supervised by Professor Håvard Rue. Hanan is also a lecturer at King Saud University and the founder of Sorat Alardh, a space-tech startup that harnesses Earth observation data for environmental and health applications. Her work has been recognized with several honors, including a grant from the Communications, Space & Technology Commission (CST) and her selection as a finalist in the Falling Walls competition.

• Geospatial data analysis and health surveillance
• Spatio-temporal disease data
• Statistics and spatial epidemiology
• Geospatial modeling
• Disease mapping

Hanan Albarqi obtained a master’s degree in Pure Mathematics from the University of Illinois at Urbana–Champaign in 2022 and a bachelor’s degree in Mathematics from King Khalid University in 2017.

Analysis and applied PDEs.

Hanchen Gan received the M.S. degree in microelectronics from the Institute of Microelectronics, Chinese Academy of Sciences (IMECAS), Beijing, China, in 2024, where he was affiliated with the National Key Laboratory of Integrated Circuit Manufacturing—3D Integration and System Packaging Laboratory. 
From March to October 2024, he worked at Cadence as a Product Validation Engineer. He worked in the CPG Group, validating the Clarity 3D full-wave electromagnetic solver. From November 2024 to August 2025, he was a visiting student with the GBDTC Electromagnetic Informatics Lab at the University of Technology Sydney (UTs).

 

Hanchen’s research interests span antenna design (antenna-in-package (AiP), mmWave phased arrays, base-station antennas, transmissive metasurface antennas) and semiconductor advanced packaging (2.5D/3D packaging，signal and power integrity, die-to-die (D2D) interconnects for chiplets).

Hang Lu is a final-year Ph.D. student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), working in the Photonics Laboratory under the supervision of Prof. Boon S. Ooi. Hang has published in Light: Science & Applications, npj Nanophotonics, and APL Photonics, with her work featured as a cover article in the IEEE Journal of Selected Topics in Quantum Electronics. She also has experience in designing augmented-reality (AR) display systems and is broadly interested in advanced photonic integration and device–application co-design.

Hang's research focuses on engineering optoelectronic devices and photonic integration circuits for applications in optical communication, illumination, and hardware security. She has hands-on experience in optical design and modeling, nanofabrication, and experimental device prototyping, and has led the development of integrated optoelectronic devices and chips for secure and scalable photonic applications.

Hani Al Majed has a Bachelor's degree in Electrical Engineering from the University of Illinois at Urbana-Champaign (2019–2023) and is currently pursuing a Master's in Electrical and Computer Engineering at KAUST (2023–2025) under the supervision of Professor Bernard Ghanem. His career includes internships at IBM, the National Center for Supercomputing Applications (NCSA), Discovery Partners Institute, and KAUST, where he worked on projects related to variational quantum models, 3D body reconstruction, sequence learning for epidemiological forecasting, and applications of physics-informed neural operators.

Hani's current research interests encompass machine learning workflow automation, physics-informed neural operators, AI for Science and chemistry.

• Sep 2021 – Jun 2025

  B.S. in Electronic Science and Technology,

  Beijing University of Posts and Telecommunications (BUPT), Beijing, China

• Sep 2025 – Present

  MS/PhD Student in Electrical Engineering,

  King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Photonics and Optoelectronics

Hans obtained a B.S. in Applied Mathematics from the Federal University of Rio Grande, and  holds a M.S. in Statistics from the Federal University of São Carlos and the University of São Paulo, in Brazil. He also worked as a data scientist for a global credit bureau.

His research focuses on Latent Gaussian Models, Bayesian Computation, Cross Validation and Point Process models.

Haoyang Li is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST) supervised by Prof. Xin Gao. His research focuses on developing AI-driven methods to integrate multimodal biomedical data, spanning spatial multi-omics, histopathology, and single-cell genomics, with the goal of decoding spatial heterogeneity in diseases and advancing diagnosis, prognosis, and biomarker discovery for personalized therapies. His research has been published in prestigious journals and conferences such as Nature Machine Intelligence, Nature Communications, Science Advances, Bioinformatics, IEEE TMI, and MICCAI. One of his first-authored papers in Nature Communications is recognized as Editor’s Highlight and the 50 best papers in 'Biotechnology and methods'. In addition, Haoyang had two internships at Yale University and Stanford University as visiting student researcher in 2023 and 2024. He has served as a teaching assistant and guest lecturer for the Ministry of Interior in Saudi Arabia, Saudi Aramco, and KAUST. Currently, he serves as an editorial board member and a guest editor for Molecular & Cellular Biomechanics and Biomedical Informatics.

Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical research.

Professional Profile

●       03/2023 - 09/2023, Research Intern, Stellantis & IMT Atlantique

●       05/2022-09/2022, R&D Intern, Invisensing

●       06/2021-08/2021, Research Intern, Institut Photovoltaïque d’Île-de-France

Education

● 01/2024 – Present, Ph.D. student in Photonics Lab, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

● 09/2022 – 12/2023, Second Master’s degree in Optical Networks and Photonics Systems, Université Paris-Saclay & Institut Polytechnique de Paris, Palaiseau, France.

●09/2020 – 12/2023, Engineering degree of Theoretical and Applied Optics, Institut d'Optique Graduate School - Université Paris-Saclay, Palaiseau, France.

●09/2017 – 06/2021, Bachelor of Science and Engineering in Photoelectric Information Science, Huazhong University of Science and Technology, Wuhan, China.

• Fiber Optic Sensing
• Optoeletronics
• Photonics

He received his M.S. degree in Computer Science from Shenzhen University in 2022, where he was honored with the China National Scholarship and Outstanding Graduate Award. In 2022, he interned at Jarvis Lab, Tencent, and was a visiting student at Norwegian Biometrics Laboratory (NBL), Norwegian University of Science and Technology (NTNU). In 2024, he joined Meta AI research scientist internship where he contributing to the development of large-scale foundation models, including autoregressive diffusion and LLM-driven multimodal systems. Haozhe Liu is (co-)first author of over 10 papers in top-tier venues, with more than 1,000 citations. He has participated in large-scale pre-training efforts, scaling models across thousands of GPUs, with research outcomes integrated into commercial products. His work was recognized as the best paper in the NeurIPS 2023 workshop. He is the reviewer for CVPR, ICCV, ECCV, ICML, AAAI, and MICCAI.

Haozhe's research focuses on multimodal generative models, particularly image and video generation, with the long-term goal of developing Physical AI and world models.

Haozheng HE is currently a MS/Ph.D. student at the Sensors Lab, in the CEMSE, King Abdullah University of Science and Technology (KAUST). He received the Bachelor of Engineering degree in Integrated Circuits and Systems from the University of Electronic Science and Technology of China, Chengdu, China, in 2023.

His research lies in Computer Architecture, Embedded Systems, Artificial Intelligence and FPGA