Prof. GAO, Xuefeng 高 雪 峰 教授

Prof. GAO, Xuefeng 高 雪 峰 教授

Prof. GAO, Xuefeng 高 雪 峰 教授
Professor

BSc (Peking University)
PhD (Georgia Institute of Technology)

Research Interests :
* Stochastic Models and Operations Research
* Reinforcement Learning
* Financial Engineering
* Generative Models

Office: Room 606, William M.W. Mong Engineering Building
Tel: (852) 3943-8242
Email: xfgao@se.cuhk.edu.hk

=> Prof. Gao’s Personal home page

Biography

Prof. Xuefeng Gao received his B.S. in Mathematics from Peking University, China in 2008, and his Ph.D. in Operations Research from Georgia Institute of Technology, USA in 2013. He joined the Department of Systems Engineering and Engineering Management (SEEM) in 2013. Currently he is a full professor and serves as the Director of the M.Sc. Programme in E-Commerce and Logistics Technologies in SEEM.
Prof Gao’s research interests include Applied Probability, Operations Research, Reinforcement Learning, Generative Diffusion Models and Financial Engineering. He has published papers in leading journals such as Operations Research and Mathematics of Operations Research, and leading machine learning conferences such as ICML, NeurIPS and ICLR.

 

Selected Publications

B. Wang, X. Gao and L. Li, Reinforcement learning for continuous-time optimal execution: Actor-Critic algorithm and error analysis. Finance and Stochastics, 2025.

X.Gao, H. Nguyen and L. Zhu. Wasserstein convergence guarantees for a general class of score-based generative models. Journal of Machine Learning Research, 2025.

X. Gao and X.Y. Zhou. Square-root regret bounds for continuous-time episodic Markov decision processes. Mathematics of Operations Research, 2024.

X. Gao and X.Y. Zhou. Logarithmic regret bounds for continuous-time average reward Markov decision processes. SIAM Journal on Control and Optimization. 2024.

X. Gao, J. Huang and J. Zhang. Asymptotically optimal control of omnichannel service systems with pick-up guarantees. Operations Research, 2023.

X. Gao and J. Huang. Asymptotically optimal control of make-to-stock systems. Mathematics of Operations Research, 2023.

X. Gao, M. Gurbuzbalaban and L. Zhu. Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for non-convex stochastic optimization: Non-asymptotic performance bounds and momentum-based acceleration. Operations Research, 2021.