Profiles

Xingdi Zhang holds a Ph.D. in Computer Science from King Abdullah University of Science and Technology (KAUST), where he was advised by Professor Markus Hadwiger in the High-Performance Visualization Group. He received her M.S. in Computer Science from KAUST in 2022 and  B.E. from the Yingcai Honor College at the University of Electronic Science and Technology of China (UESTC) in 2020. His research bridges scientific visualization, computer graphics, differential geometry, and calculus of variations, with a focus on optimal reference frames in unsteady flow, Lagrangian vortex corelines, and observer-relative flow visualization.

• Scientific Visualization & Computer Graphics: Observer-relative vortex extraction, optimal reference frames, Lagrangian vortex corelines, discrete geometry, and graphics engines.
• Mathematics: Differential Geometry, Calculus of Variations.
• AI4science: learning-based flow feature extraction

Yasir Ghunaim is a Ph.D. candidate in Computer Science at KAUST in the Image and Video Understanding Lab (IVUL), supervised by Professor Bernard Ghanem. His research focuses on efficient machine learning for scientific and medical applications, with an emphasis on improving data, training, and model efficiency to enable scalable and practical ML systems.

Yasir holds an M.S. in Computer Science from KAUST and a B.S. in Electrical and Computer Engineering from Virginia Tech. Prior to his graduate studies, he gained over seven years of experience in automation systems.

Yasir's 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.