讲座题目：The Unreasonable Effectiveness of Mathematics in Biosciences
主讲人：Guo-Wei Wei 教授
主持人：刘宗华 教授
开始时间：2024-07-04 10:00
讲座地址：闵行校区物理楼314会议室
主办单位：物理与电子科学学院
报告人简介：
Guowei Wei received his Ph.D. degree from the University of British Columbia and is currently an MSU Research Foundation Professor at Michigan State University. He has pioneered novel computational methods that integrate profound mathematical structures with deep learning, leading to victories in D3R Grand Challenges, a worldwide competition series in computer-aided drug design, and the discovery of SARS-CoV-2 evolutionary mechanisms. He has earned numerous awards and honors, including the elected fellow of AIMBE. In addition, he has partnered with world premier pharmaceutical companies and the U.S. Food and Drug Administration (FDA) on drug discovery. Dr. Wei has served extensively on various national and international panels, committees, and journal editorships. Many of his former trainees now hold faculty positions in research universities worldwide.
报告内容：
In E.P. Wigner’s famous essay "The unreasonable effectiveness of mathematics in the natural sciences," he did not anticipate the resilience of biological sciences to mathematics in 1960. Since the 1960s, it has been unreasonably challenging to apply contemporary mathematics to contemporary biology, such as cellular biology, molecular biology, chemical biology, genomics, and genetics. Artificial intelligence (AI) has fundamentally changed the landscape of science, engineering, and technology in the past decade and holds great promise for discovering the rules of life. However, AI-based biological discovery encounters challenges arising from the intricate complexity, high dimensionality, nonlinearity, and multiscale biological systems. We tackle these challenges with a mathematical AI paradigm. We have introduced persistent cohomology, persistent spectral graphs, persistent path Laplacians, persistent sheaf Laplacians, and evolutionary de Rham-Hodge theory to significantly enhance AI's ability to tackle biological challenges. Using our mathematical AI approaches, my team has been the top winner in D3R Grand Challenges, a worldwide annual competition series in computer-aided drug design and discovery for years. By further integrating mathematical AI with millions of genomes isolated from patients, we discovered the mechanisms of SARS-CoV-2 evolution and accurately forecast emerging dominant SARS-CoV-2 variants months in advance.
