I am completing my PhD degree in Computer Science at The Chinese University of Hong Kong (CUHK) under the supervision of Prof. Zhi-Quan (Tom) Luo, with expected graduation in March 2025. Previously, I received my M.S. & B.E. degrees with honors from Huazhong University of Science & Technology, conducted research at Cornell University with Prof. John E. Hopcroft, and gained industry experience at Microsoft Research and Tencent AI & Robotics X.
I develop intelligent agents that reliably interact with complex, uncertain, human-in-the-loop environments. Through advances in uncertainty quantification, reinforcement learning (RL), and LLM reasoning & planning, I bridge foundational theory with scalable algorithms for trustworthy decision-making in critical domains under data scarcity.
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My research is dedicated to developing trustworthy AI agents that operate reliably in complex, uncertain, and dynamic environments involving human interaction. By advancing fundamental theory in uncertainty quantification, exploration strategies, and LLM reasoning and decision-making, I design scalable algorithms that enhance the trustworthiness of AI agents. This work bridges foundational theory with practical applications across reinforcement learning (RL) and large language models (LLMs), contributing to both theoretical advancements and impactful real-world solutions.
My contributions have led to significant advancements in reinforcement learning, language model reasoning, and human-AI interaction. Recognized at premier venues such as ICML, NeurIPS, ICLR, AISTATS, ISMP, and INFORMS, my work has also received honors like the 2024 Daoyuan Forum Best Paper and the 2024 IEEE SAM Best Student Paper Award.
To enhance the reliability and robustness of AI agents, I advanced methods for representing and handling uncertainty:
Ensemble Sampling Theory:
Neural Ensemble++ Architecture:
Efficient learning under data scarcity is crucial for trustworthy AI agents operating in real-world environments:
HyperAgent Framework:
Memoire Framework and Divergence-Augmented Policy Optimization:
Frameworks for Unknown Repeated Games:
Transparency and explainability are key aspects of trustworthiness, addressed through advancements in language-based reasoning:
Uncertainty-Guided Search Strategies in LLMs:
Hospital Referral Agent:
Human-AI Collaboration Frameworks:
This research collectively enhances the trustworthiness of AI agents by addressing fundamental challenges and providing scalable, reliable solutions applicable to real-world scenarios.