Profiles

Aldossary research interests include computer vision, machine learning, deep learning, computational neuroscience, and brain–computer interfaces. She is particularly interested in EEG representation and behavior learning, with applications in clinical EEG analysis and brain–computer interfacing.

Danish Raja is a PhD candidate in the Advanced Semiconductor Laboratory (ASL) at King Abdullah University of Science and Technology (KAUST).

He received his Master's degree from the Indian Institute of Technology (IIT) Jodhpur and his Bachelor's degree in Electronics and Communication Engineering from the Islamic University of Science and Technology (IUST) Kashmir.

Danish’s research experience includes ferroelectric and negative-capacitance devices for energy-efficient computing and memory applications, as well as machine-learning-based semiconductor device modeling.

His current research focuses AI hardware, with an emphasis on the Compute-in-Memory paradigm, energy-efficient device and architecture design, and high-performance computing systems.

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

With research and work experiences in interdisciplinary studies, he is interested in technologies that help people communicate better and that helps knowledge disseminates faster, especially from the perspective of education. He is now working on the Nano-visualization of the mitochondrion as a breakthrough point to the long term goal.

Divyanshu is a Ph.D. candidate in the Electrical and Computer Engineering department at KAUST. He received his M.Tech degree in VLSI from the Indian Institute of Technology (IIT) Mandi, India. He has worked as a visiting student at the Innovative Technologies Lab and the Integrated Circuits and System Group at KAUST. 

His research interests include spintronics devices and circuits, in-memory computing using beyond CMOS devices, and hardware security primitives.

Donia Besher obtained her bachelor's degree in Mathematics from Sorbonne University, France. She is now a master's student in Statistics at King Abdullah University of Science and Technology (KAUST), working under Prof. Raphaël Huser's supervision.

Donia is interested in deep learning for time series forecasting, spatiotemporal modeling, and financial time series analysis.

Echo Ziyi Yang is a Ph.D. student at King Abdullah University of Science and Technology (KAUST), conducting research under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Laboratory (IVUL) within the Center of Excellence for Generative AI. She obtained her Master’s and Bachelor’s degrees from Harbin Institute of Technology (HIT), China.

Her research interests focus on Generative AI, autonomous agents, machine learning, and reinforcement learning.

Egor Shulgin is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Professor Peter Richtárik. His work has appeared at venues including ICML, ICLR, AISTATS, and UAI. During his PhD, he conducted research internships at Apple and Samsung AI Center in Cambridge, UK. He received his BSc in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology in 2019.

Egor's research focuses on optimization for machine learning, including distributed and federated training, privacy-preserving learning, and practical optimizers for modern deep learning. 

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.

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).

Fatimah Zohra is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), where she works in the Image and Video Understanding Lab (IVUL) under the supervision of Prof. Bernard Ghanem. Before her Ph.D., she was a Computer Scientist at Adobe working on a large-scale distributed platform. She received her M.S. in Computer Science from KAUST and her B.S. in Computer Science from The University of Texas at Austin.

Fatimah's research focuses on fine-grained representation learning for vision-language and video models. She developed β-CLIP (CVPR 2026), which achieves state-of-the-art fine-grained vision-language alignment among methods trained without hard negatives, and HyperGS, a feedforward Gaussian video representation that encodes orders of magnitude faster than prior methods. 

Ganesh Mainali is a Ph.D. candidate in Electrical and Computer Engineering at KAUST, where he conducts research in the Advanced Semiconductor Laboratory under the supervision of Prof. Xiaohang Li. Before joining KAUST, he earned an M.S. in Electronic Engineering from Kwangwoon University, South Korea, in 2021.

Ganesh's research focuses on ultrawide-bandgap β-Ga2O3 devices and circuits for monolithic power integration. His work includes monolithic gate-driver ICs, a pseudo-source-gated MOSFET with record intrinsic gain, on-chip thermocouples for junction-temperature sensing, and vertical Schottky barrier diodes designed for high-temperature operation. In addition to power electronics, he has research experience designing low-noise amplifiers, phase shifters, attenuators, and baluns in RF electronics. He has also worked on a dual-band Wi-Fi module. He is currently working toward the development of an ultrawide-bandgap-based power converter module.

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.

Grigory Malinovsky is a Ph.D. candidate in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in the Optimization and Machine Learning Lab.

Grigory holds a Master of Science in Applied Mathematics and Computational Science from KAUST and a Bachelor's degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, where he completed his thesis under the guidance of Boris Polyak.

During his Ph.D., he completed a six-month internship at Samsung R&D Institute UK, and collaborated during research visits with Sebastian Stich at CISPA, as well as with Samuel Horváth and Eduard Gorbunov at MBZUAI. His work has been published in leading machine learning conferences such as NeurIPS, ICLR, ICML, AISTATS, AAAI, and UAI. Alongside his research, he has contributed to teaching graduate-level optimization courses and actively mentored research interns, leading to multiple joint publications.

Grigory's research focuses on optimization and its applications in machine learning, particularly federated learning, distributed optimization, and deep learning theory.

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

Haibo Wu is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST) and a member of the Biostatistics Group (BIOSTATS) research group led by Prof. Hernando Ombao. Through both methodological innovation and applied research, Haibo Wu has contributed to the statistical analysis of complex data arising from neuroscience and financial systems. His work combines rigorous theoretical development, statistical inference, and practical applications, providing new tools for uncovering dynamic interactions that are often missed by traditional stationary or Fourier-based approaches.

Haibo's research focuses on statistical methods for analyzing nonstationary multivariate time series, with applications in neuroscience.

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.