Events
Colloquium
Physics for Deep Learning: Towards a Theoretical Foundation
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Speaker: Yuhai Tu (Flatiron) Senior Research Scientist, Center for Computational Biology & Center for Computational Neuroscience Flatiron Institute Thursday, February 5, 2026 4:00PM - 5:00PM Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=36cecfab-954a-4e35-b444-b3d100ee1029 |
Talk summary: Artificial Neural Network (ANN) and Machine Learning (ML) have received a huge amount of attention among physicists triggered by the 2024 Nobel Physics Prize to John Hopfield and Geoff Hinton. Historically, ANN-based ML was developed based on ideas from neuroscience and statistical physics. However, the recent successes of deep learning neural networks (DLNN) are mainly driven by large amount of data and exponentially growing computing capability. As a result, despite its successes in many different disciplines, DLNN remains largely a black box – it is unclear how it learns and whether what it learns is generalizable. In the past several years, we have been trying to develop a theoretical framework based on statistical physics and stochastic dynamical systems theory to study DLNN. In this talk, we will first give a broad introduction of the ANN-based ML emphasizing on the role of physics in its development followed by describing some of our recent progress in understanding DLNN in two related areas – learning dynamics and generalization.
Speaker bio: Yuhai Tu joined the Center for Computational Biology with a joint position at the Center for Computational Neuroscience in May 2025 as a Senior Research Scientist. Yuhai’s work focuses on Statistical Physics, Molecular/Cellular Biology, Neuroscience, and Machine Learning. His pioneering work on collective phenomena in active systems (flocking dynamics) in the 90’s won him (together with John Toner and Tamas Vicsek) the APS Lars Onsager Prize in 2020. Since 2000, his research interests shift to biological physics. He has made seminal contributions in many areas of biological physics including algorithm development and statistical analysis for high throughput transcriptome data (microarray analysis); quantitative modeling of signal transduction and motility in bacterial chemotaxis; and thermodynamics of nonequilibrium biochemical networks. His recent work focuses on three directions: (1) dynamics of biological networks — biochemical networks for signal transduction and neural networks for coding and computation; (2) thermodynamics of information processing in biological systems; (3) statistical physics of machine learning.
Yuhai Tu graduated from the School of Gifted Young at University of Science and Technology of China in 1987. He came to the US under the CASPEA program and got his Ph.D. in theoretical physics from University of California San Diego (UCSD) in 1991. After 3 years as the Division Prize Fellow at Caltech, he joined IBM T. J. Watson Research Center from 1994-2025 and served as head of the theory group during 2002-2014. He joined Flatiron Institute in 2025. He is an American Physical Society (APS) fellow (elected 2004), and an American Association for the Advancement of Science (AAAS) fellow (elected 2020). He served as Vice-Chair/Chair of APS Division of Biological Physics (DBIO) during 2016-2018.
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