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Colloquium on Digital Transformation Science

  • April 815, 3 pm CT

    Recent Advances in Analysis of Implicit Bias of Gradient Descent on Deep Networks

    Matus Telgarsky, Assistant Professor of Computer Science, University

    AI-Enabled Deep Mutational Scanning of Interaction between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor

    Diwakar Shukla, Assistant Professor, Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign


    The purpose of this talk is to highlight three recent directions in the study of implicit bias, a promising approach to developing a tight generalization theory for deep networks interwoven with optimization. The first direction is a warm-up with purely linear predictors: here, the implicit bias perspective gives the fastest known hard-margin SVM solver! The second direction is on the early training phase with shallow networks: here, implicit bias leads to good training and testing error, with not just narrow networks but also arbitrarily large ones. The talk concludes with deep networks, providing a variety of structural lemmas that capture foundational aspects of how weights evolve for any width and sufficiently large amounts of training. This is joint work with Ziwei Ji.Matus Telgarsky is an Assistant Professor of Computer Science rapid and escalating spread of SARS-CoV-2 poses an immediate public health emergency. The viral spike protein S binds ACE2 on host cells to initiate molecular events that release the viral genome intracellularly. Soluble ACE2 inhibits entry of both SARS and SARS-2 coronaviruses by acting as a decoy for S binding sites, and is a candidate for therapeutic and prophylactic development. Deep mutational scans is one of the approaches that could provide such a detailed map of protein-protein interactions. However, this technique suffers from several issues such as experimental noise, expensive experimental protocol, and lack of techniques that could provide second or higher-order mutation effects. In this talk, we describe an approach that employs a recently developed platform, TLmutation, that could enable rapid investigation of sequence-structure-function relationship of proteins. In particular, we employ a transfer learning approach to generate high-fidelity scans from noisy experimental data and transfer the knowledge from single point mutation data to generate higher-order mutational scans from the single amino-acid substitution data. Using deep mutagenesis, variants of ACE2 will be identified with increased binding to the receptor binding domain of S at a cell surface. We plan to employ the information from the preliminary mutational landscape to generate the high order mutations in ACE2 that could enhance binding to S protein. We also aim to investigate this problem using distributed computing approaches to understand the underlying physics of the spike protein and ACE2 interaction.

    Diwakar Shukla is the Blue Waters Assistant Professor, Department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign, specializing in deep learning theory. He received a PhD at the University of California, San Diego under Sanjoy Dasgupta. He co-founded the Midwest ML Symposium in 2017 with Po-Ling Loh and organized a Simons Institute summer 2019 program on deep learning with Samy Bengio, Aleskander Madry, and Elchanan Mossel. He received an NSF CAREER Award in 2018. His research focuses on understanding the complex biological processes using novel physics-based models and techniques. He received his B.Tech and M.Tech. degrees from the Indian Institute of Technology in Bombay and his MS and PhD degrees from the Massachusetts Institute of Technology. His postdoctoral work was at Stanford University. He has received several awards for his research including the Peterson award from ACS, Innovation in Biotechnology award from AAPS, COMSEF Graduate student award from AIChE, Institute Silver Medal, and Manudhane Award from IIT Bombay.

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