Explore how mathematics and artificial intelligence are revolutionizing biosciences in this 47-minute lecture by Guo-Wei Wei. Delve into the challenges of applying AI to biological discovery, including the structural complexity of macromolecules and high dimensionality of biological variability. Learn about innovative mathematical approaches such as evolutionary de Rham-Hodge theory, persistent cohomology, and persistent spectral graph theory, which enhance AI's ability to handle large biological datasets. Discover how these techniques have been successfully applied to computer-aided drug design, predicting SARS-CoV-2 variants, and understanding viral mutations. Gain insights into the intersection of classical topology, algebraic topology, differential geometry, and graph theory with modern biological problems. Understand the importance of reducing complexity in biological data and how mathematical AI approaches are advancing our understanding of life sciences, from molecular structures to viral evolution. Read more