Greetings and Welcome to My Homepage


I am currently a Research Professor (HIC-ZJU-100 Young Professor, ZJU Qizhen Outstanding Young Scholar) at Zhejiang University, working closely with Prof. Zhan Qin and Prof. Kui Ren. Before that, I received my Ph.D. degree with honors in Computer Science and Technology from Tsinghua University, advised by Prof. Yong Jiang and Prof. Shu-Tao Xia. I received my B.S. degree with honors in Mathematics from Ningbo University, advised by Prof. Lifeng Xi. I also collaborated closely with Prof. Bo Li (from UIUC) and Dr. Zhifeng Li (from Tencent) during my Ph.D. journey.

My research mainly focuses on Trustworthy ML and Responsible AI, especially backdoor attacks/defenses and copyright protection in deep learning. My long-term goal is to make DNNs more secure and copyright-preserving during their full life cycle. Recently, I focus more on Trustworthy Large Foundation Models (e.g., GPT and Diffusion Model). I always chase for simple yet effective methods with deep insights and theoretical support.


I am always looking for highly self-motivated students and research interns to join exciting research projects on Trustworthy ML and Responsible AI in our group. Besides, I am always willing to work together on interesting projects with external collaborators. Drop me an email if you are interested!


  • 01/2024: I am invited as an Area Chair of ACM MM 2024.
  • 01/2024: So glad to become the Qizhen Outstanding Young Scholar at Zhejiang University.
  • 01/2024: Three papers about backdoor learning (2 backdoor defenses and 1 backdoor for XAI) are accepted by ICLR 2024 (with one Spotlight)! Their codes will be released soon. Congrats to Xiong Xu, Kunzhe Huang, Mengxi Ya, Tinghao Xie, Xiangyu Qi, and all collaborators!
  • 12/2023: So exciting to be selected as one of the KAUST Rising Stars in AI.
  • 09/2023: Two papers are accepted by NeurIPS 2023. Their codes will be released soon.

Useful Resources

BackdoorBox: A Python Toolbox for Backdoor Attacks and Defenses

Github Repo about Backdoor Learning Resources