Profiles

Salma Kharrat is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), where her research focuses on machine learning under limited information, spanning federated learning, multi-agent reinforcement learning, and large language models.

Her research has been published in leading AI and machine learning venues, including AISTATS, EMNLP, and ECAI, with contributions such as FilFL, DPFL, and ACING, which address client selection in federated learning, decentralized personalization, and instruction optimization for large language models. During her Ph.D., she was recognized with the KAUST Dean’s List Award.

Salma earned her M.S. in Computer Science from KAUST and her engineering degree from the National School of Computer Science in Tunisia, where she ranked among the top students.

In addition to her research, she has been actively involved in teaching and mentoring, serving as an instructor and teaching assistant for machine learning and AI courses at KAUST and across Saudi Arabia, and mentoring student research projects through KAUST Academy. 

Salma research focuses on developing principled algorithms for decentralized learning, personalization under heterogeneity, and black-box optimization, with the goal of advancing scalable and robust intelligent systems.

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.

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.

Xingdi Zhang is a Computer Science Ph.D. student in Computer Science at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Markus Hadwiger in the High-Performance Visualization Group. Xingdi received his M.S. (2022) in Computer Science from KAUST and his B.E. (2020) from the University of Electronic Science and Technology of China (UESTC). His doctoral research bridges Riemannian geometry, physics, and deep learning for objective vortex extraction and flow visualization.

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

• Visualization: Riemannian Geometry based observer relative vortex extraction;
• Computer Vision: depth estimation, point cloud understanding;
• Computer Graphics: discrete geometry, graphics engine, etc.

Xinge Yang is a Ph.D. candidate at King Abdullah University of Science and Technology (KAUST), working with Prof. Wolfgang Heidrich. He received his B.S. in Physics from the University of Science and Technology of China (USTC) in 2020.

Xinge Yang's research focuses on differentiable optics and computational imaging, with applications to next-generation optical design paradigms that combine inverse design and deep learning optimization algorithms, as well as end-to-end imaging and display systems that integrate optics with AI-based image processing.

Yidan is a Ph.D. student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020.

Yidan is interested in developing novel computational imaging systems in both hardware and software.

Yulian Wu is a Ph.D. candidate in Computer Science at KAUST, advised by Francesco Orabona. She has published in top venues including NeurIPS, ICML, COLT, Science Advances, and AISTATS, and was recognized as a NeurIPS Top Reviewer. She received the Best Presentation Award at the Free Rein Global Youth AI Forum. She is also actively involved in the research community, serving as an Organizing Committee Member and Session Chair for the KAUST Rising Stars in AI Symposium 2025 and 2026.

Yulian's research investigates trustworthy machine learning and interactive decision-making, with a focus on differential privacy, robustness, and heavy-tailed feedback in bandits, reinforcement learning, and RLHF.

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