AMSS-YMSC-BIMSA Joint Seminar on Progress of Topology and Its Applications

组织者 Organizer:Guo-Wei Wei (MSU), Stephen Yau (Tsinghua), Haibao Duan (AMSS), Yong Lin (YMSC), Jianzhong Pan (AMSS), Jie Wu (BIMSA), Fei Han (NUS), Kelin Xia (NTU), Chao Zhou (NUS)
时间 Time:9:00a.m UTC+8,MAR 24 2022 Thur
地点 Venue:ZOOM:388 528 9728 (PW BIMSA)


Aim and Scope of the Joint Seminar:    

In the area of topology, there has been a lot of exciting progress in recent years. Topological tools and ideas have been used in arithmetic geometry, for instance, topological cyclic homology, and in low dimensional topology, like constructions of new knot and link invariants using homotopy theory and so on. On the other hand, topology has found exciting applications in science and technology. In mathematical physics, topics like topological quantum field theory, quantum anomaly cancellation, topological T-duality, topological insulators, topological order, and others, have made tremendous progress. In modern technology, topology has also played prominent roles. In particular, TDA (topological data analysis) has demonstrated great potential for big data and has been widely used in the study of robotics, materials, chemistry, biology, drug design and discovery. More and more topological tools and ideas have been used in machine learning and deep learning models. The aim of the seminar is to invite top experts and young researchers to introduce and share their progress in the study of topology and its applications. We expect that the seminar will bring potential collaborations and broaden the horizon of young students.


Upcoming Talk:

How Math and AI are revolutionizing biosciences    
    

Date: MAR 24 2022 Thur                      

Time: 9:00a.m UTC+8            

ZOOM: 388 528 9728 (PW BIMSA)                       

Link:  http://www.bimsa.cn/newsinfo/624195.html        


    

Abstract:

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, biochemistry, genomics, and genetics, has been quite limited. Artificial intelligence (AI) has fundamentally changed the landscape of science, technology, industry, and social media in the past few years and holds a great future for discovering the rules of life. However, AI-based biological discovery encounters challenges arising from the structural complexity of macromolecules, the high dimensionality of biological variability, the multiscale entanglement of molecules, cells, tissues, organs, and organisms, the nonlinearity of genotype, phenotype, and environment coupling, and the excessiveness of genomic, transcriptomic, proteomic, and metabolomic data. We tackle these challenges mathematically. Our work focuses on reducing the complexity, dimensionality, entanglement, and nonlinearity of biological data. We have introduced evolutionary de Rham-Hodge, persistent cohomology, persistent Laplacian, and persistent sheaf theories to model complex, heterogeneous, multiscale biological systems and thus significantly enhance AI's ability to handle biological data. 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 with millions of genomes isolated from patients, we reveal the mechanisms of SARS-CoV-2 evolution and transmission and accurately forecast emerging SARS-CoV-2 variants.    


   

About Speaker:                

Professor Guo-Wei Wei is a Foundation Professor of Michigan State University. He is a top scientist in the world in many disciplines, including mathematics, physics, chemistry, biology, computer science, engineering, AI, machine learning and data science, editor for a number of international journals, and a panelist or reviewer for a large number of funding agencies in various countries. Professor Wei has 288 publications, including 27 research articles on COVID-19, with more than 155 76 citations.