Ph.D. candidate in Computer Science at City University of Hong Kong
I study learning and decision-making under uncertainty, with a focus on bandit algorithms, feedback design, and adversarial robustness.
⋆ Equal contribution.
Identifying the best arm by adaptively combining reward and preference feedback, including their acquisition costs.
In Proceedings of the 40th Annual Conference on Neural Information Processing Systems
Understanding how limited, bounded fake feedback can steer bandit algorithms toward an attacker's target arm.
In Proceedings of the 40th Annual Conference on Neural Information Processing Systems
Memory-efficient sampling and rounding for influence maximization under partition constraints.
Our paper WEREWOLF: Reputation-Aware Red-Teaming for Self-Organizing LLM Multi-Agent Systems was accepted to Findings of EMNLP 2026.
Our paper One Rounding Fits All: Memory-Efficient Approximation Algorithms for Partition-Constrained Influence Maximization was accepted to KDD 2026.
I am a Ph.D. candidate in Computer Science at City University of Hong Kong, advised by Prof. Jinhang Zuo. Previously, I worked as a research assistant with Prof. Sibo Wang at the Chinese University of Hong Kong from July to October 2024.
I received my bachelor’s degree in Computer Science and Technology from the School of the Gifted Young at the University of Science and Technology of China, where I was advised by Prof. Xue Chen and Prof. Jinhang Zuo.
2026
ICLR and NeurIPS.
2025
ICML and NeurIPS.
Tag frequency
127 accepted submissions
Updated