李传浩，清华大学工业工程系助理教授。在哈尔滨工业大学获得英语-机械设计制造及其自动化双学士学位和机械电子工程硕士学位，在美国弗吉尼亚大学获得计算机科学博士学位。研究兴趣集中于多智能体系统的学习、决策与博弈，致力于通过学习与优化算法以及协作机制的设计，提升系统在多维度上的效率与性能。

2025.07 – 至今，清华大学工业工程系，助理教授

2023.10 – 2025.06，美国耶鲁大学统计与数据科学系，博士后

2018.08 – 2023.08，美国弗吉尼亚大学，计算机科学，博士

2016.09 – 2018.07，哈尔滨工业大学，机械电子工程，硕士

2012.08 – 2016.07，哈尔滨工业大学，英语-机械设计制造及其自动化，学士

李传浩老师每年计划招收博士生、硕士生及本科生科研助理。感兴趣的同学请将简历发送至 chuanhao-li{at}tsinghua.edu.cn。

招生期待：欢迎自我驱动、富有好奇心、乐于沟通的同学加入团队。希望你具备扎实的数理基础与编程能力，并在机器学习、强化学习、运筹优化等方向具备理论或应用背景。

研究聚焦于在动态、不确定环境中能够推理、交互和学习的多智能体系统，融合机器学习、优化方法和算法博弈论，主要包括：

· 设计在线协作学习算法，提高样本利用和通信效率；

· 构建激励相容的机制，将自利型智能体的行为与系统整体目标对齐；

· 设计结合大语言模型的分层决策架构及其上下文工程与训练方法，进一步增强系统的智能水平与适应能力。

研究旨在使多智能体系统（如工业智能系统和推荐系统）能够在非结构化环境下实现稳健、灵活的协同决策，并在此过程中兼顾样本与通信效率，以及对激励约束的识别与适应能力。

（完整列表请参见：https://www.chuanhao-li.com/publications/）

1. 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

INFORMS Workshop on Market Design @EC 2024, AutoRL Workshop @ICML 2024 [paper] [code]

2. Communication-Efficient Federated Non-Linear Bandit Optimization

Chuanhao Li*, Chong Liu*, Yu-Xiang Wang

International Conference on Learning Representations (ICLR) 2024 [paper]

3. Incentivized Truthful Communication for Federated Bandits

Zhepei Wei*, Chuanhao Li*, Haifeng Xu, Hongning Wang

International Conference on Learning Representations (ICLR) 2024 [paper]

4. PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference

Kendong Liu*, Zhiyu Zhu*, Chuanhao Li*, Hui Liu, Huanqiang Zeng, and Junhui Hou

Neural Information Processing Systems (NeurIPS) 2024 [project page]

5. Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment

Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang

International Conference on Learning Representations (ICLR) 2023 [paper]

6. Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

Chuanhao Li, Hongning Wang

International Conference on Artificial Intelligence and Statistics (AISTATS) 2022 [paper] [code]

7. Communication Efficient Distributed Learning for Kernelized Contextual Bandits

Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang

Neural Information Processing Systems (NeurIPS) 2022 [paper]

8. Communication Efficient Federated Learning for Generalized Linear Bandits

Chuanhao Li, Hongning Wang

Neural Information Processing Systems (NeurIPS) 2022 [paper] [code]

9. When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution

Chuanhao Li, Qingyun Wu, Hongning Wang

International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2021 [paper] [code]

Unifying Clustered and Non-stationary Bandits

Chuanhao Li, Qingyun Wu, Hongning Wang

International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 [paper] [code]

10. Unifying Clustered and Non-stationary Bandits

Chuanhao Li, Qingyun Wu, Hongning Wang

International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 [paper] [code]