I am a first-year PhD student in Data Science at City University of Hong Kong, advised by Prof. Xiangyu Zhao in the Applied Machine Learning Lab. My research focuses on scalable reasoning in large language models, with emphasis on chain-of-thought compression and efficient inference for mathematical reasoning.
Before starting my PhD, I completed an MSc in Venture Creation at CityU and a BEng in Electrical Engineering and Automation at Nantong University. My research combines tensor methods, reinforcement learning, and generative AI to build models that reason reliably under computational constraints.
Research Interests
Scalable Reasoning: Chain-of-thought compression and efficient inference in LLMs
Tensor Methods: High-dimensional data compression and structure discovery
Generative AI: Diffusion models and generative recommendation systems
RL Post-Training: Curriculum learning and verifier-guided optimization
Publications
Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPOUnder Review