My research focuses on online learning and sequential decision-making for building efficient, scalable, and trustworthy machine learning systems. I work on both system-driven learning theory problems and theory-guided online algorithms for real-world systems, including LLM serving, Mixture-of-Experts (MoE), and edge AI. A central theme of my work is integrating learning theory with system design to enable intelligent, resource-aware computation.

Openings: I am seeking Research Assistants starting in Summer/Fall 2025 and PhD students starting in Fall 2026. If you are interested, please email me your CV and transcripts. For more information, please visit this page.

Bio

I am an Assistant Professor in the Department of Computer Science at City University of Hong Kong. Prior to this, I was a joint postdoc at California Institute of Technology and University of Massachusetts Amherst, working with Prof. Adam Wierman and Prof. Mohammad Hajiesmaili. I received my Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, advised by Prof. Carlee Joe-Wong. Before that, I completed my B.E. in Communication Engineering at Nanjing University of Posts and Telecommunications.

My CV can be found here.

News

• May 2025: Two papers are accepted to ICML 2025 and one paper is accepted to KDD 2025!
• Jan. 2025: Our work on bandits robust to adversarial attacks is accepted to ICLR 2025.
• Dec. 2024: Our work on robust combinatorial contextual bandits is accepted to INFOCOM 2025.
• Dec. 2024: Our work on heterogeneous multi-agent bandits with hints is accepted to AAAI 2025.