题目:Mathematical AI for Molecular Sciences:from topological data analysis to topological deep learning
报告人：Prof. Kelin Xia(Nanyang Technological University)
时间: 2026年7月16日16：00-17：00
报告地点：伍卓群楼第一报告厅
校内联系方式：郭斌bguo@jlu.edu.cn
报告摘要：
A central challenge in artificial intelligence (AI)-driven molecular science lies in efficiently representing molecular data and developing learning architectures that capture intrinsic structure-function relationships. In this work, we introduce advanced mathematics-based molecular representations and learning frameworks. Molecular structures and interactions are encoded using high-order topological and algebraic representations, including Rips complexes, Alpha complexes, Neighborhood complexes, Dowker complexes, Hom-complexes, Tor-algebras, Rhomboid tiling, Sheaves, Categories, etc. Building on these foundations, we design physics-informed geometric and topological deep learning models that systematically integrate high-order, multiscale, and periodic information of molecular systems. These models have been successfully applied to diverse molecular datasets across chemistry, biology, and materials science, demonstrating their versatility and effectiveness in uncovering complex structural-functional relationships.
报告人简介：
Dr. Kelin Xia obtained his Ph.D. degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar in 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, Nature Methods, Nature Machine Intelligence, Science Advances, npj Computational Materials, ACS nano, TPAMI, ICML, etc. He has served as editor-in-chief of “Theory in Biosciences”, associated editor for “Computational Physiology and Medicine-Frontiers” and “Computational and Structural Biotechnology Journal”, editorial boards of “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 & 2025.
