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Speaker: Jinchao Xu (KAUST/Penn State) Wednesday, November 6, 2024 11:30 am: Lunch (Kitchen) 12:00 pm: Talk (Seminar Room #1327) |
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Abstract:
Bio: Jinchao Xu is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST), where he also serves as the Director of the KAUST Innovation Hub in Shenzhen and the KAUST-SRIBD Joint Lab for Scientific Computing and Machine Learning. An esteemed scholar, Xu is a Fellow of the Society for Industrial and Applied Mathematics (SIAM), the American Mathematical Society (AMS), the American Association for the Advancement of Science (AAAS), the European Academy of Sciences (EURASC), and the Academia Europaea. His research focuses on numerical methods for partial differential equations, multigrid methods, and the intersection of machine learning with scientific computing.
Xu is widely recognized for his contributions to numerical analysis, particularly in developing preconditioners and algorithms that bear his name, including the Bramble-Pasciak-Xu (BPX) preconditioner and the Hiptmair-Xu (HX) preconditioner. He has recently turned his focus to deep learning, contributing to the theoretical understanding and practical applications of neural networks, including pioneering work on the MgNet architecture for image classification. Xu has published nearly 200 research papers and continues to push the boundaries of computational mathematics and machine learning.
