Profiles

Shaopeng is a Ph.D. student in Computer Science at KAUST. Before that, He was as an algorithm engineer at the trustworthy AI research group at JD Explore Academy, JD.com, Inc. He received MPhil in Computer Science from The University of Sydney, Australia, and B.Sc in Mathematics from the South China University of Technology, China.

His research lies in trustworthy machine learning, especially the security and privacy aspects of machine learning. He is interested in using mathematical principles to identify and mitigate security and privacy risks in real-world machine learning systems.

Shuai Lu is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Professor Gabriel Wittum and working in the Modeling and Simulations Lab. He completed his B.Eng. in Mechanical Engineering from China University of Mining and Technology (CUMT) in 2015. He received his master’s degree in Solid Mechanics at Beihang University in 2018.

• Long-Form Video Understanding.
• Temporal Action Detection, Action Recognition.
• Video-Language Grounding.

Sumyyah Toonsi is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), with a focus on bioinformatics. Her work integrates text mining, genetic risk prediction, and causal inference to advance understanding of complex biomedical data. She applies computational methods to support data-driven discoveries in health and disease.

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.

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.

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.

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

Bachelor's degree from Wuhan University.

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

• Digital signal.
• Image processing.
• Machine learning
• Wireless communications