Hello! I am a first-year Ph.D. student at Carnegie Mellon University (CMU) in Electrical and Computer Engineering (ECE). I am fortunate to be advised by Andrea Zanette. Prior to that, I completed my undergraduate program from Yao Class at IIIS, Tsinghua University.

My research passion lies in designing scalable and efficient algorithms with theoretical insights for practical machine learning problems. Currently, I focus on the problems related to foundation models, including but not limited to alignment, fine-tuning, and safety. During my undergraduate studies, my concentration was on reinforcement learning.

I am open to collaborations. If you have any ideas which we both might be interested in, please feel free to reach out!

Selected Research

Model-based return-conditioned supervised learning

Research intern at UW (Feb. 2023 - Aug. 2023).

Supervisor: Simon S. Du.

We proposed model-based return-conditioned supervised learning (MBRCSL), a novel offline RL framework that is able to do trajectory stitching while retaining the strength of return-conditioned supervised learning (RCSL) to avoid Bellman completeness requirements. [website]

Networked Markov Potential Games

Remote research intern at Caltech (Feb. 2022 - Feb. 2023).

Supervisor: Adam Wierman.

We proposed networked Markov potential games (NMPG) as a more practical relaxation of Markov potential games (MPG), and designed a localized actor-critic algorithm with provable finite-sample bound. [arXiv]

News

• Sep 2024: I have joined CMU as a PhD student! I look forward to future collaborations with my advisor, Andrea Zanette.
• Jan 2024: One paper accepted by ICLR 2024!
• Nov 2023: One paper accepted by NeurIPS 2023 FMDM workshop (oral)! Thanks for efforts from all collaborators!
• Aug 2023: Participate in UAI 2023.
• May 2023: One paper accepted by UAI 2023! Thanks for generous support from Dr. Zaiwei Chen, Yiheng Lin and Prof. Adam Wierman.
• Feb 2023: Begin my spring visit at UW. Look forward to cooperation with Prof. Simon Shaolei Du.