I am an Applied Scientist at Amazon AWS AI Labs, where I work on the post-training of agentic foundation models based on large language models (LLMs). Prior to Amazon, I worked at Sony AI America with Prof. Peter Stone and Peter Wurman on developing a superhuman racing agent Gran Turismo Sophy for a highly realistic racing game.

I earned my Ph.D. in Computer Science and Engineering from the University of Connecticut, where I was advised by Prof. Fei Miao and focused on artificial intelligence. Before that, I worked on game theory in the Dynamic Systems Control Lab advised by Prof. Chengbin Ma at the University of Michigan–Shanghai Jiao Tong University Joint Institute. I received my B.E. degree from Nanjing University.

My current research interests include large language models, artificial general intelligence, reinforcement learning, and generative AI.

News

• [2025/6] Our paper “Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications” is accepted by 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• [2024/12] Our project, Amazon Q Developer code review, is announced by AWS CEO Matt Garman at AWS re:Invent 2024. website
• [2024/9] I am excited to join Amazon AWS AI Labs as an applied scientist.
• [2024/8] Our team’s paper “A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo” received the Outstanding Paper Award at the 1st annual Reinforcement Learning Conference (RLC) 2024.
• [2024/1] Our paper “Real-time Human Presence Estimation For Indoor Robots” is accepted by 2024 IEEE International Conference on Robotics and Automation (ICRA).
• [2024/1] Our paper “Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation” is accepted by IEEE Robotics and Automation Letters (RA-L). Available on arxiv, website.
• [2024/1] Our paper “What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?” is accepted by Transactions on Machine Learning Research (TMLR). Available on arxiv, website.
• [2023/10] Our paper “A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles” is accepted by IEEE Transactions on Intelligent Transportation Systems. Available on arxiv, website.
• [2023/8] I am excited to join Sony AI as a research scientist.
• [2023/6] Our paper “Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing” is accepted by ACM Transactions on Cyber-Physical Systems (TCPS).
• [2023/5] Our paper “Robust Multi-Agent Reinforcement Learning with State Uncertainty” is accepted by Transactions on Machine Learning Research (TMLR).
• [2023/5] I receive the CSE Predoctoral Fellowship.
• [2023/5] I give an invited Seminar “Safe, Stable, and Robust Multi-Agent Reinforcement Learning for Connected Autonomous Vehicles” at MIT.
• [2023/2] Our paper “Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles” gets the Best Paper Award in the DCAA workshop at AAAI 2023, Washington, DC. Available on arxiv.
• [2023/1] Our paper “Uncertainty Quantification of Collaborative Detection for Self-Driving” is accepted by the 2023 IEEE International Conference on Robotics and Automation (ICRA), available on arxiv, website.
• [2023/1] Our paper “Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios” is accepted by the 2023 IEEE International Conference on Robotics and Automation (ICRA), available on arxiv.
• [2022/8] I get the General Electric (GE) fellowship of excellence. The GE Fellowship for Excellence program is established to recognize excellence of current graduate students and to facilitate their completion of the Ph.D. program.
• [2022/7] Our paper “Towards Safe Autonomy in Hybrid Traffic: The Power of Information Sharing in Detecting Abnormal Human Drivers Behaviors” is presented in the AI4TS workshop at the 31st International Joint Conference On Artificial Intelligence (IJCAI 2022).
• [2022/7] Our paper “DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services” is accepted by ACM Transactions on Cyber-Physical Systems. (available online).
• [2022/5] Our paper “Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles” is presented on the 2022 IEEE International Conference on Robotics and Automation (ICRA), available online.