Published papers include 'Multi-objective Linear Reinforcement Learning with Lexicographic Rewards', 'Provable In-Context Vector Arithmetic via Retrieving Task Concepts', 'Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples', and 'Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning'. Won 2 prizes.
Research Experience
Involved in multiple research projects on reinforcement learning and large language models, with several papers published at international conferences.
Education
PhD Student, Department of Computer Science, City University of Hong Kong (CityUHK)
Background
Research interests include Large Language Models, Reinforcement Learning, Optimization Theory, etc.