About Me
I am a Postdoctoral Research Associate in the Department of Mathematics at the University of Oxford, advised by Professor Christoph Reisinger, and a member of the Erlangen AI Hub.
I completed my Ph.D. in Industrial Engineering & Operations Research at UC Berkeley in Summer 2025, advised by Professor Xin Guo.
My research interests are at the intersection of stochastic control, reinforcement learning, game theory, and machine learning.
Education
Ph.D. Industrial Engineering & Operation Research, UC Berkeley (Berkeley, CA), 2020 - 2025
- Major: Industrial Engineering & Operation Research
- Advisor: Prof. Xin Guo
- Master of Science in Industrial Engineering and Operations Research (Awarded: May 2021)
B.S. Mathematics, New York University (Shanghai, New York), 2016 - 2020
Work Experience
- Research Scientist Intern, Amazon, Middle Mile Product & Technology (Santa Clara, CA) May 2023 - Aug 2023
- Research Scientist Intern, Amazon, Middle Mile Product & Technology (Santa Clara, CA) May 2022 - December 2022
Publications
- X Li, An α-Potential Game Framework for Non-Cooperative Dynamic Games: Theory and Algorithms, 2025. University of California, Berkeley, PhD dissertation [pdf]
- X Guo, X Li, Y Zhang, Distributed Games with Jumps: an α-Potential Game Approach, preprint, 2025 [arXiv] [code]
- X Guo, X Li, and R Xu, Fast Policy Learning for Linear Quadratic Regulator with Entropy Regularization, SIAM Journal on Control and Optimization, 2025 [arXiv]
- X Guo, X Li, Y Zhang, An α-Potential Game Framework for N-player Games, SIAM Journal on Control and Optimization, 2025 [arXiv] [Journal]
- X Guo, X Li, C Maheshwari, S Sastry, and M Wu, Markov α-Potential Games, IEEE Transactions and Control, 2025 [arXiv] [Journal]
- H Gu, X Guo, TL Jacobs, P Kaminsky, and X Li, Transportation Market Rate Forecast Using Signature Transform, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2024 [Conference Version]
Finalist for the 2024 Daniel H. Wagner Prize
INFORMS Journal of Applied Analytics, 2025 [arXiv] - H Gu, X Guo, and X Li, Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation, Revision at Applied Probability Journals, 2025 [arXiv]
- Z Zong, X Li, and P Sanaei, Effects of nutrient depletion on tissue growth in a tissue engineering scaffold pore, Physics of Fluids, 2021 [Journal]
Invited Talks & Poster Sessions
- SIAM Conference on Financial Mathematics and Engineering (FM25), Miami, FL, 2025
- Advances in Stochastic Control and Reinforcement Learning, Banff, Canada, 2025
- 2025 Western Conference on Mathematical Finance, USC, CA, 2025
- INFORMS 2024, Seattle, WA, 2024
- DIMACS 2024 Workshop on Forecasting, Rutgers University, NJ, 2024
- Joint Statistical Meetings, Portland, OR, 2024
- Banff International Station, Canada, 2024 (Online)
- Cyber Risk and Insurance France-Berkeley Conference, UC Berkeley, CA, 2024
- 58th Annual Conference on Information Sciences and Systems, Princeton University, NJ, 2024
- 4th ACM International Conference on AI in Finance, NY, 2023
- 10th International Congress on Industrial and Applied Mathematics, Waseda University, Tokyo, Japan, 2023 (Online)
- INFORMS 2023, Phoenix, AZ, 2023
- Berkeley-Columbia Workshop, Columbia University, NY, 2023
- Western Conference on Mathematical Finance, UC Berkeley, CA, 2023
Recent Honors & Awards
- Berkeley Marshall-Oliver-Rosenberger Fellowship, 2024
- Berkeley IEOR First Year Fellowship, 2020
Teaching Experience
Graduate Student Instructor, UC Berkeley
- IEOR 221: Introduction to Financial Engineering, Spring 2025
- IEOR 221: Introduction to Financial Engineering, Spring 2024
- IEOR 222: Financial Engineering Systems I, Spring 2023
- IEOR 242/142: Application in Data Analysis, Spring 2022, Spring 2021