Haotian Gu
Department of Mathematics, UC Berkeley
About Me
Hi! I am a fifth-year Ph.D. candidate in applied mathematics at UC Berkeley. I am jointly advised by Prof. Xin Guo and Prof. Fraydoun Rezakhanlou. Before entering UC Berkeley, I completed my Bachelor degree in mathematics at the University of Hong Kong.
My research focuses on the intersection of stochastic control, game theory and reinforcement learning. Meanwhile, I also work on theoretical machine learning regarding robustness and transferability.
Google Scholar---LinkedIn---Resume---Email
Publications
Mean-field Control and Reinforcement Learning
Dynamic programming principles for learning Mean-field controls [arXiv]
Haotian Gu, Xin Guo, Xiaoli Wei, Renyuan Xu.
Accepted by Operation Research.
Q-Learning for Mean-Field Controls with Convergence and Complexity Analysis [arXiv]
Haotian Gu, Xin Guo, Xiaoli Wei, Renyuan Xu.
Accepted by SIAM Journal on Mathematics of Data Science.
Short Version accepted by Workshop on Theoretical Foundations of Reinforcement Learning, ICML 2020.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach [arXiv]
Haotian Gu, Xin Guo, Xiaoli Wei, Renyuan Xu.
Submitted to Mathematics of Operation Research.
Adversarial Robustness
An SDE Framework for Adversarial Training, with Convergence and Robustness Analysis [arXiv]
Haotian Gu, Xin Guo, Xinyu Li
Federated Learning
BoFL: Bayesian Optimized Local Training Pace Control for Energy Efficient Federated Learning
Hongpeng Guo, Haotian Gu, Zhe Yang, Xiaoyang Wang, Eun Kung Lee, Nandhini Chandramoorthy, Tamar Eilam, Deming Chen, Klara Nahrstedt.
Accepted by the 23rd ACM/IFIP International Middleware Conference.
Management Science
Relay Freight Index: Freight Market Rate Prediction with Signature Transform
Haotian Gu, Xin Guo, Tim Jacobs, Philip Kaminsky
Available upon request
PDE and Numerical Analysis
An Efficient Model Reduction Method for Solving Viscous G-equations in Incompressible Cellular Flows [arXiv]
Haotian Gu, Jack Xin, Zhiwen Zhang.
Accepted by SIAM Journal on Scientific Computing.
Analysis on Hybrid Fractals [arXiv]
Patricia Alonso-Ruiz, Yuming Chen, Haotian Gu, Robert Strichartz, Ziri Zhou.
Accepted by Communications on Pure and Applied Analysis
Invited Talks
Numerical Analysis Seminar: April 2022, University of Hong Kong, Virtual Conference
Student Probability/PDE Seminar: April 2021, University of California, Berkeley
Workshop on the Intersection of Machine Learning, Control and Games: November 2020, INFORMS Annual Meeting 2020, Virtual Conference
Workshop on Theoretical Foundations of Reinforcement Learning: July 2020, International Conference on Machine Learning 2020, Virtual Conference
Seminar on Applied Mathematics: May 2018, University of Hong Kong, Pok Fu Lam
Cornell Conference on Analysis, Probability, and Mathematical Physics on Fractals: June 2017, Cornell University, Ithaca
Teaching Experience
MATH 170 Mathematical Methods for Optimization: Fall 2020, teaching assistant for Prof. Lawrence Evans
MATH 195 Calculus of Variations and Optimal Control: Spring 2021, teaching assistant for Prof. Lawrence Evans
IEOR 222 Financial Engineering Systems: Spring 2022, teaching assistant for Prof. Thibaut Mastrolia
Industry Experience
Research Scientist Intern, Amazon
Remote, May 2021 - August 2021
Middle Mile Planning, Research & Optimization Sciences
Quantitative Research Intern, Citadel Securities
Chicago, June 2022 - August 2022
Systematic FICC