Fouts Family Professor
Education
- Ph.D. Electrical Engineering (2009), University of Maryland
- B.S. (with Highest honors) Electrical Engineering (2004), Chu Kochen Honors College, Zhejiang University, China
Expertise
- Simulation
- Stochastic Optimization
- Stochastic Control
- Machine Learning
About
Enlu Zhou is a Fouts Family Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech, Dr. Zhou served as an Assistant Professor at the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign from 2009 to 2013. She has served as an associate editor for Journal of Simulation, IEEE Transactions on Automatic Control, and Operations Research. She currently serves as an associate editor for SIAM Journal on Optimization and co-Editor-in-Chief for Journal of Simulation. She is the President of the INFORMS Simulation Society from 2024 to 2026.
Research
Dr. Zhou’s research interests lie in theory, methods, and applications of simulation, stochastic optimization, stochastic control, and reinforcement learning. She currently works on (i) modeling and algorithm design for data-driven optimization and decision-making problems, and (ii) theoretical study of empirically successful machine learning algorithms. The application areas of her research include robotics, systems biology, and financial engineering. Her research has received the Best Theoretical Paper awards at the Winter Simulation Conference, AFOSR Young Investigator award, NSF CAREER award, and INFORMS Outstanding Simulation Publication Award.
Awards and Honors
- Fouts Family Professorship, Georgia Tech, 2024-2029
- Best Theoretical Paper Award, Winter Simulation Conference, 2022
- INFORMS Outstanding Simulation Publication Award, 2020
- Finalist for Best Paper Award, Journal of Global Optimization, 2016
- Fouts Family Early Career Professorship, Georgia Tech, 2016-2019
- Career Award, National Science Foundation, 2014
- AFOSR Young Investigator Award, 2012
- Best Theoretical Paper Award, Winter Simulation Conference, 2009
Representative Publications
- Alexander Shapiro, Enlu Zhou, Yifan Lin, Yuhao Wang, “Episodic Bayesian Optimal Control with Unknown Randomness Distributions“, Operations Research, 2025.
- Yifan Lin, Yuhao Wang, Enlu Zhou, “Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate“, Operations Research, 2025.
- Yingke Li, Enlu Zhou, and Fumin Zhang, “A Distributed Bayesian Data Fusion Algorithm with Uniform Consistency“, IEEE Transactions on Automatic Control, 2024
- Yuhao Wang and Enlu Zhou, “Optimal Computing Budget Allocation for Data-driven Ranking and Selection“, INFORMS Journal on Optimization, 2024.
- Enlu Zhou, “Data-driven Simulation Optimization in the Age of Digital Twins: Challenges and Developments”, invited tutorial, Winter Simulation Conference (WSC), 2024.
- Tianyi Liu, Yifan Lin, and Enlu Zhou, “Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data“, SIAM Journal on Optimization, 2023.
- Alexander Shapiro, Enlu Zhou, and Yifan Lin, “Bayesian Distributionally Robust Optimization“, SIAM Journal on Optimization, 2023.
- Yuhao Wang* and Enlu Zhou, “Bayesian Risk-Averse Q-Learning with Streaming Observations“, Advances in Neural Information Processing Systems (NeurIPS), 2023.
- Di Wu, Yuhao Wang, and Enlu Zhou, “Data-driven Ranking and Selection under Input Uncertainty”, Operations Research, 2022.
- Yifan Lin, Yuxuan Ren, and Enlu Zhou, “Bayesian Risk Markov Decision Processes“, Advances in Neural Information Processing Systems (NeurIPS), 2022.
- Tianyi Liu, Zhehui Chen, Enlu Zhou, and Tuo Zhao, “A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization”, Stochastic Systems, 2021.
- Yingke Li, Tianyi Liu, Enlu Zhou, and Fumin Zhang, “Bayesian Learning Model Predictive Control for Process-Aware Source Seeking”, IEEE Control Systems Letters, 2021.
- Joshua Hale, Helin Zhu, and Enlu Zhou, “Domination Measure: A New Metric For Solving Multiobjective Optimization“, INFORMS Journal on Computing, 2020.
- Helin Zhu, Tianyi Liu, and Enlu Zhou, “Risk Quantification in Stochastic Simulation under Input Uncertainty“, ACM Transactions on Modeling and Computer Simulation, 2020.
- Sait Cakmak, Rahul Astudillo, Peter Frazier, and Enlu Zhou, “Bayesian Optimization of Risk Measures“, Advances in Neural Information Processing Systems (NeurIPS), 2020.
- Di Wu, Helin Zhu, and Enlu Zhou, “A Bayesian Risk Approach to Data-driven Stochastic Optimization: Formulations and Asymptotics“, SIAM Journal on Optimization, 2018. (2020 INFORMS Outstanding Simulation Publication Award)