计算与应用讨论班——Mathematical AI for Molecular Sciences

报告人：夏克林（Nanyang Technological University）

时间：2025年6月16日，10:00-11:00

地点：海纳苑2幢203

摘要：Artificial intelligence (AI) based Molecular Sciences have begun to gain momentum due to the great advancement in experimental data, computational power and learning models. However, a major issue that remains for all these AI-based learning models is the efficient molecular representations and featurization. Here we propose advanced mathematics-based molecular representations and featurization. Molecular structures and their interactions are represented by high-order topological and algebraic models (including Rips complex, Alpha complex, Neighborhood complex, Dowker complex, Hom-complex, Tor-algebra, Rhombille tiling, etc). Mathematical invariants (from persistent homology, Ricci curvature, persistent spectral, Analytic torsion, algebraic variety, etc) are used as molecular descriptors for learning models. Further, we develop geometric and topological deep learning models to systematically incorporate molecular high-order, multiscale, and periodic information, and use them for analysing molecular data from chemistry, biology, and materials.

报告人简介：Dr. Kelin Xia (夏克林) obtained his Ph.D. degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar in the department of Mathematics, Michigan State University from Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University. He joined Nanyang Technological University in Jun 2016 and was promoted to associate professor in Mar 2023. His research focused on Mathematical AI for molecular sciences. He has published more than 90 papers in journals and conferences, including SIAM Review, Science Advances, npj Computational Materials, ACS nano, TPAMI, ICML, KDD, etc. He has served as associated editor for “Computational Physiology and Medicine – Frontiers” and “Computational and Structural Biotechnology Journal”, editorial boards of “Theory in Biosciences”, “Scientific report” and “Journal of Physics: Complexity”, and editorial advisory board of “Journal of Chemical Information and Modeling” and “Patterns. He is Stanford and Elsevier World’s Top 2% Scientists 2024.

联系人：蔺宏伟（hwlin@zju.edu.cn）