About
I’m currently a Postdoctoral Associate in the Department of Statistics and Data Science at Yale University, working with Professors Zhuoran Yang and Dirk Bergemann. I completed my Ph.D. in Computer Science at the University of Virginia, where I was advised by Professor Hongning Wang.
Research Interest
I am interested in multi‑agent systems that reason, interact, and learn in dynamic and uncertain settings—whether the individual agents’ objectives are aligned or not. My work blends machine learning, optimization, and algorithmic game theory to create:
- communication‑efficient collaborative learning protocols that reduce sample and bandwidth costs,
- incentive‑compatible mechanisms that align self‑interested agents with system goals, and
- hierarchical decision architectures augmented with large language models, leveraging common-sense knowledge to interpret unstructured inputs and adapt to novel scenarios.
These methods enable robust, adaptive cooperative decision-making in multi-agent systems—such as recommender systems and cyber-physical systems—ensuring sample and communication efficiency, as well as incentive awareness, under unstructured conditions.
Selected Publications
- STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh Sharifi, Dirk Bergemann, and Zhuoran Yang
arXiv [paper] [code] - Incentivized Truthful Communication for Federated Bandits
Zhepei Wei*, Chuanhao Li*, Haifeng Xu, Hongning Wang
ICLR 2024 [paper] - Communication Efficient Distributed Learning for Kernelized Contextual Bandits
Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang
NeurIPS 2022 [paper] - Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Chuanhao Li, Hongning Wang
AISTATS 2022 [paper] [code] - Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
AISTATS 2021 [paper] [code]