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        Department of Systems Engineering and Engineering Management

                       The Chinese University of Hong Kong

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Date: Friday, December 12, 2025, 3:30 pm to 4:30 pm HKT

Venue: ERB 616, The Chinese University of Hong Kong

Title: Bayesian Learning for Data-driven Dynamic Stochastic Optimization

Speaker: Prof. ZHOU Enlu, Georgia Institute of Technology

Abstract:

In many dynamic stochastic optimization models, including multi-stage stochastic program, stochastic optimal control (SOC), and Markov decision process (MDP), distributions of the randomness are never precisely known in practice and are typically estimated by data. Assuming a parametric form of the randomness distribution, we take a Bayesian approach to learn the unknown distribution parameter from streaming data and propose a Bayesian risk re-formulation of the original problem. The Bayesian posterior distribution can be treated as a state augmented to the original state, leading to a higher-dimensional continuous-state problem. While this approach theoretically provides the optimal control policy, it can be challenging to solve numerically. Therefore, we further propose an episodic approach that only updates the posterior periodically and solves a Bayesian counterpart problem under the fixed posterior in each period. Theoretical convergence results and computational methods will be discussed.

Biography:

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.

Everyone is welcome to attend the talk!