Profiles

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.

Talha Ariff was awarded his BEng/MEng degree in Biomedical Engineering from City, University of London in 2022, where he focused on optoelectronic devices for use in medical and chemical sensing.
He is now a Ph.D. student in the Photonics Lab at KAUST, researching further applications for optoelectronic sensors in desert agriculture and undersea communication cables.

Marine Ecology & Desert agriculture, Sensors, Photonics & Optoelectronics

Tingang Liu is a Ph.D student in Electrical and Computer Engineering (ECE) at KAUST under the supervision of Prof. Xiaohang Li. He got his bachelor’s degree in 2022 from University of Electronics Science and Technology of China and his master’s degree in 2023 from King Abdullah University of Science and Technology.

Tingang's research focuses on ultra-wide bandgap Al-rich AlGaN based power devices for next generation power systems. He has experience on material epitaxy by MOCVD, various material characterization tools, and advanced device fabrication process.

Wenbo Yan received the B.Eng. degree in Materials Science and Engineering from South China University of Technology, China, in 2021, and M.S. degree in Material Science and Engineering in 2022 from King Abdullah University of Science and Technology (KAUST). He is currently pursuing the Ph.D. degree in Electrical and Computer Engineering at KAUST under the supervision of Prof. Boon S. Ooi.

Wenbo's research focuses on VCSEL design, coherence engineering, thermal management, optical communication, and photonic device fabrication.

William He is a Ph.D. student at the Integrated Photonics Laboratory under the supervision of Prof. Yating Wan at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, William did his undergraduate study in University of California, Santa Barbara (UCSB), ranked in the top ~10%  and obtained Dean's Honors for his outstanding performance. William has actively participated in research since his second year in UCSB and has been a co-author of three high-quality journals. Besides academic research experiences, William has also been actively participating in several volunteer work, extracurricular activities, and one internship in Pacific Transformer Corporation in United States.

William's research interests include integrated Si photonics and its applications in data centers. 

His research interest is mainly in Bayesian and computational Statistics, currently working on Directional Statistics and applications with R-INLA. He is also interested in Deep/Machine Learning algorithms.

Xiangpeng Ou is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Yating Wan. He obtained a B.E. in Optoelectronic Information Science and Engineering from the University of Electronic Science and Technology of China (UESTC) in 2018, and an M.S. in Microelectronics and Solid-State Electronics from the Institute of Microelectronics (IME), Chinese Academy of Sciences (CAS), in 2022.

His research focuses on silicon photonics and quantum-dot on-chip lasers, spanning monolithic and heterogeneous III–V/Si integration, tunable and mode-locked lasers, and integrated silicon FMCW LiDAR systems. He has published his work as a first or co-first author in prominent journals including Optica, Light: Science & Applications, IEEE Journal of Selected Topics in Quantum Electronics, eLight, Optics Express, and Advanced Materials Technologies.

Xiangpeng's research is advancing semiconductor laser technologies and large-scale integrated photonic systems. During his Ph.D. he worked on the design, fabrication, and characterization of novel quantum-dot on-chip lasers for AI data centers and integrated FMCW LiDAR applications.

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.

Xiaotian Jin is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), working under the supervision of Professor David Bolin. He received his B.S. degree in computer science from Wenzhou-Kean University, China, in 2020 and his M.S. degree in Statistics from KAUST in 2021.

Xiaotian’s research focuses on the intersection of computational statistics, geostatistics theory. He specializes in developing unified, computationally efficient frameworks for modeling non-Gaussian data, addressing the limitations of traditional Gaussian-based systems in capturing complex, real-world data characteristics.

His work spans the mathematical analysis of convergence properties in sampling algorithms, the design of hierarchical spatio-temporal models, and the application of these methods to high-dimensional environmental datasets, such as global oceanographic profiles.

Xiaoyi Song received his Bachelor's degree in Electronic Science and Technology from the University of Electronic Science and Technology of China (UESTC) in 2022 and is currently pursuing a Master's degree at UESTC. Before joining KAUST, he engaged in research on magneto-optical integration and integrated photonics for three years. His research experience encompasses the deposition, design, and characterization of magneto-optical materials and photonic devices, including phase shifters and nonreciprocal components such as optical circulators. He has published a research paper in Photonics Research.

Xiaoyi’s research interests are in Silicon photonics and its applications, including on-chip light source, LiDAR, and nonreciprocal optical devices.

Xin Yao comes from the School of Gifted Young at the University of Science and Technology of China (USTC), which is designed for the most talented young students in China. Xin majored in Photoelectric Information Science and Engineering, during the past three years study, he won Silver Award for excellent students (Top~10％) and Freshmen Scholarship. Because of the experience in ‘Jiuzhang’ quantum computing laboratory, he is interested in the Si photonics and integrated optical circuits, which show promise for stable quantum computation. He started his MS/PhD study in IPL in the August 2023.

Silicon photonics, on-chip lasers, heterogeneous integration

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.

Xiran Zhang is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST). He received his B.S. in Mathematics and Applied Mathematics from the University of Science and Technology of China (USTC) in June 2021 and his M.S. in Statistics from KAUST in December 2022. His research lies at the intersection of statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems. Key words of his work include distributed CPU/GPU computing, parallel algorithms, uncertainty quantification for massive spatial data, and spatio-temporal cross-covariance modeling.

Xiran has his work published or presented at major international conferences, including IPDPS, JSM, and SC. In addition to his research, he has been actively involved in teaching and mentoring, serving as a teaching assistant for several STAT courses at KAUST and at King Fahad Security College for the Ministry of Interior. He has received several honors, including the Al-Kindi Statistics Top Quals Student Award in 2021 and the KAUST Dean’s List Award in 2024 and 2025.

During his doctoral studies, he has developed high-performance computational frameworks for credible and confidence region detection in massive geostatistical datasets, designed optimized implementations on distributed runtime systems such as PaRSEC and StarPU, and worked on GPU-accelerated scientific computing pipelines. He has also contributed to task-based parallel computing for statistical software through RCOMPSs, an open-source runtime system for R, and has collaborated with international research teams including the Barcelona Supercomputing Center and the University of Colorado Denver.