Profiles

Xiaochuan Gou is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Di Wang and Prof. Xiangliang Zhang. His research focuses on spatio-temporal data mining, with an emphasis on explainability and efficiency in deep learning models for traffic forecasting and urban computing. He is the lead author of TraffiDent, an open-source multimodal traffic dataset that integrates incident information and supports explainable modeling, and has published multiple papers in top-tier international conferences such as CIKM, WSDM, Big Data, and SIGSPATIAL.

Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic planning, urban planning and social network related to the modern life.

Xiaofeng Xu is a Ph.D. candidate in the AMCS program at KAUST, under the supervision of Professor Jinchao Xu. His research lies at the intersection of traditional numerical methods for partial differential equations (PDEs) and modern machine learning approaches. He is particularly interested in developing efficient and provably convergent training algorithms for neural networks in numerical PDEs.

Xiaofeng received his Bachelor's degree in Mathematics and Computer Science with First Class Honors from the Hong Kong University of Science and Technology (HKUST), and his Master’s degree from the Pennsylvania State University.

Xiaofeng Xu research focuses on the intersection of traditional numerical methods for partial differential equations (PDEs) and modern machine learning approaches. He is particularly interested in developing efficient and provably convergent training algorithms for neural networks in numerical PDEs.

I am a PhD student in computer science specializing in Riemannian geometry and flow visualization.
In my free time and during my bachelor stage,  I am working  in graphics engines and computer vision.

Research interest

He is interested in observer relative flow visualization,  extreme-scale data, visualization, computer graphics.

• Signal processing.
• Learning method over complex.
• Dynamic structures
• Low-complexity and energy-efficient VLSI signal processing system design.

Yan Wang is a M.S./Ph.D. student in the Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). She obtained her bachelor's degree of Communication Engineering from the University of Electronic, Science, and Technology of China (UESTC) in 2024.

Photonics and optoelectronics, Ultrawide bandgap semiconductor

Yara Banda is an M.S./Ph.D. student at the Photonics Lab in the Electrical and Computer Engineering Department (CEMSE) at King Abdullah University of Science and Technology (KAUST). She received her B.Sc. degree from King Abdulaziz University (Jeddah, Saudi Arabia) in 2021.

I am currently pursuing a Ph.D. in Computer Science at King Abdullah University for Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem. My research focuses on applying machine learning to solve real-world challenges, with an emphasis on efficient training and inference of models. This includes improving data, training, and model efficiency to create more scalable and effective solutions. I hold an M.S. in Computer Science from KAUST and a B.S. in Electrical and Computer Engineering from Virginia Tech. I have over 7 years of experience in automation systems prior to starting my M.S. degree.

I am interested in exploring techniques for efficient training and inference of machine learning models, with a focus on improving data efficiency, training efficiency, and model efficiency.

She holds a Master’s Degree in Data Science from the University of Jeddah and a Bachelor's Degree in Computer Science with a specialization in Software Engineering from King Abdulaziz University. She began her career as a Digital Repository Coordinator at King Abdullah University for Science and Technology (KAUST), where she was responsible for developing data analysis algorithms and building data models. Later, she transitioned to the role of Data Science Coordinator and Teaching Assistant at the SDAIA-KAUST Center of Data Science & AI, where she focused on AI content development, automation, and machine learning training initiatives.

Her research interests focus on generative AI, vision/text multimodality, and deep learning

Yasmine's research interests include controller design, observer design, virtual sensors, scientific machine learning, UAV control in non-inertial frames.

Yiming Yang is a Ph.D. candidate at the KAUST Integrated Microwaves Packaging Antennas and Circuits Technology Research Group under the supervision of Professor Atif Shamim. Before joining KAUST, Yiming obtained a bachelor's degree in electronic science and technology from the University of Electronic Science and Technology of China (UESTC).

Yiming Yang‘s research interests include frequency selective surfaces, reconfigurable intelligent surfaces, and meta-surfaces in combination with additive manufacturing, time modulation, and other fabrication and reconfiguration methods.

• Nanofabrication of Semiconductors
• N/MEMS design and dynamics
• Control Theory
• Vibration
• Mechatronics

Yue Wang is a Ph.D. candidate at the Photonics Laboratory at King Abdullah University of Science and Technology (KAUST), under the mentorship of Prof. Boon S. Ooi. She earned her B.Eng. in Electronic Science and Technology from the University of Electronic Science and Technology of China (UESTC) in 2020. She received her master's degree in Electrical Engineering from KAUST in 2021.

Yue Wang's research focuses on semiconductor optoelectronics, high-speed color-converting luminescent devices, and optical wireless communication. She specializes in characterizing color-converting materials, designing and fabricating luminescent optoelectronic devices, and developing optical wireless communication systems.

Yue Wang has explored emerging luminescent materials such as perovskites, metal-organic frameworks (MOFs), and organic fluorophores, enabling Gb/s visible light communication and wavelength-division multiplexing with enhanced channel capacity. To relieve the stringent requirements of pointing, acquisition, and tracking in underwater optical wireless communication channels, she employed scintillating fibers and luminescent solar concentrators for wide field-of-view, high-speed photodetection. Her work also extends to optical amplification, where she has developed a high-gain visible-light amplifier based on perovskite quantum dots, potentially addressing the optical loss during long-distance optical data transmission.

• Optical wireless communication.
• Embedded systems.
• Hardware & software co-design.

Zahrah Alnasser obtained her bachelor's degree from Dammam College for Women and her master's degree in pure mathematics from the University of Illinois - Urbana-Champaign.

Real analysis and applied PDEs.

Zhiyuan Liu is a Ph.D. candidate in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Xiaohang Li. He received his bachelor’s degree from the University of Electronic Science and Technology of China in 2020. His research focuses on the fabrication processes and performance optimization of III-nitride optoelectronic devices, covering areas such as micro-LEDs, deep ultraviolet (DUV) LEDs, and ultraviolet photodetectors. His work emphasizes device structural design, physical modeling, micro/nano-fabrication techniques, and advanced surface and interface engineering to enhance the efficiency and reliability of III-nitride devices. To date, Zhiyuan Liu has published over 30 SCI journal papers, including 10 first-author articles in leading international journals such as Light: Science & Applications, Optics Letters, and Journal of Materials Chemistry C.

Zihang Xiang is a 4th-year Ph.D. candidate at King Abdullah University of Science and Technology(KAUST), advised by Di Wang. His current research is on privacy-preserving data analysis. He is interested in pushing the boundaries of differential privacy via principled approaches in broad machine learning applications.

Differential privacy

Zinan XIAO is a Ph.D. student at Photonics Laboratory at King Abdullah University of Science and Technology (KAUST). 
Before he joined the Photonics Laboratory, he worked as an optical hardware engineer in the industry, focusing on designing and testing optical splitters. 
He received his Master of Science degree at Imperial College London, UK. 
He obtained his bachelor's degree in optoelectronic information engineering at Chongqing University. 

Sensors, Photonics and optoelectronics

Bachelor's degree from Wuhan University.

Edge computing, federated learning, machine learning systems, and resilient computing.