SNAPP Seminar || Enlu Zhou (Georgia Institute of Technology) || November 24, 2025

Speaker: Enlu Zhou (Georgia Institute of Technology) || November 24, 2025

Title: Understanding two machine learning algorithms through the lens of continuous-time approximation

Abstract: Some widely-used machine learning algorithms have demonstrated great empirical success, yet often lacking rigorous theoretical justification. In our work, we studied two such algorithms: momentum stochastic gradient descent, and natural policy gradient with the reuse of historical trajectories. By continuous-time approximation of the algorithmic iterations and analysis of the resulting ODEs and SDEs, we investigated theoretical convergence properties of these algorithms, offering insights and justification for their empirical behaviors. Our analysis also inspired new improvements to these existing algorithms.

Speaker's Bio: Enlu Zhou is a Fouts Family Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She received the B.S. degree with highest honors in electrical engineering from Zhejiang University, China, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park. Prior to joining Georgia Tech, she was an assistant professor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign. She is a recipient of the Best Theoretical Paper award at the Winter Simulation Conference, AFOSR Young Investigator award, NSF CAREER award, and INFORMS Outstanding Simulation Publication Award. She has been on the editorial board of Journal of Simulation, IEEE Transactions on Automatic Control, Operations Research, and SIAM Journal on Optimization. She is currently a co-Editor-in-Chief for Journal of Simulation. She is the President of the INFORMS Simulation Society from 2024 to 2026. Her research interests lie in theory, methods, and applications of simulation, stochastic optimization, and stochastic control.