A faculty candidate for the Ellen and Vincent Florenza '75 Endowed Chair in Health Innovation and Technology position earned his Ph.D. degree from the University of British Columbia in 1996.  He was awarded a postdoctoral fellowship from the NSERC of Canada to pursue his postdoctoral work at the University of Houston. In 1998, he joined the faculty of the National University of Singapore and was promoted to Associate Professor in 2001. In 2002, he relocated to Michigan State University, where he is an MSU Foundation Professor of Mathematics, Electrical and Computer Engineering, and Biochemistry and Molecular Biology. His current research interests include mathematical foundations of data science and biosciences, deep learning, drug discovery, and computational geometry, topology, and graph. He has served extensively in a wide variety of national and international panels, committees, and journal editorships. His work was reported in numerous news and media articles.   

Research Talk: "How Mathematical AI is Transforming Biosciences."  

Mathematics underpins fundamental theories in physics such as quantum mechanics, general relativity, and quantum field theory. Nonetheless, its success in modern biology, namely cellular biology, molecular biology, chemical biology, genomics, and genetics, has been quite limited. Artificial intelligence (AI) has fundamentally changed the landscape of science, engineering, and technology in the past decade and holds a great future 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 by a mathematical AI paradigm. We have introduced evolutionary de Rham-Hodge theory, persistent cohomology, persistent spectral graphs, persistent path Laplacian, and persistent sheaf theories 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 uncovered the mechanisms of SARS-CoV-2 evolution and accurately forecast emerging SARS-CoV-2 variants.