About me
Hi! I’m Zechen, a PhD student in Computer Science at Duke University advised by Ronald Parr. Previously, I was a research fellow in Computer Science at Brown University, working under the guidance of Amy Greenwald. Before that, I did my Master’s degree in Computational Science at EPFL & USI in Switzerland, where I conducted research under the guidance of Volkan Cevher. Prior to graduate school, I worked as a quantitative trader.
News
Dec 4, 2025: Presenting my NeurIPS 2025 Spotlight paper in San Diego. Supported by the NeurIPS 2025 Scholar Award. Full session details on my NeurIPS paper page.
Sept 18th, 2025: My first-author paper, A Unifying View of Linear Function Approximation in Off-Policy RL Through Matrix Splitting and Preconditioning, was accepted to NeurIPS 2025 as a Spotlight presentation (top 3% of submissions).
Research Interests
Keywords: Theoretical foundations of Reinforcement Learning (RL), deep RL, Continual learning, foundation model, Large Language Model(LLM)
I study the theoretical foundations of reinforcement learning (RL) and sequential decision-making, aiming to develop a fundamental understanding of their mechanisms, bridge theory and practice, and design efficient algorithms that solve industrial problems. I am also interested in applying these insights to emerging domains such as the foundation model, with a focus on topics including reasoning, alignment, efficiency, and optimization.