Greetings and welcome to my homepage
I am currently a fifth-year Ph.D. candidate in Computer Science and Technology from Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, advised by Professor Yong Jiang and Professor Shu-Tao Xia. Before that, I received my B.S. degree with honor in Mathematics and Applied Mathematics from Ningbo University (Yangming Innovation Class) in 2018, advised by Professor Lifeng Xi.
At the beginning of my Ph.D. journey, I studied tree-based methods for their interpretability and good theoretical properties. Currently, my research mainly focuses on Trustworthy ML and AI security, especially backdoor learning, adversarial learning, data privacy, and copyright protection in deep learning. My long-term goal is to make DL-based systems more robust and secure during their full life-cycle. I always chase for simple yet effective methods with deep insights or even theoretical supports. My research is supported by the Tsinghua ‘Future Scholar’ Ph.D. Fellowship.
Currently, I am working with Professor Bo Li at UIUC as a visiting Ph.D. student (online, due to VISA issues). I was a research intern at Ant Security Lab (2021, 2022), working with Dr. Haiqin Weng and Dr. Tao Wei; a research intern at Tencent AI Lab (2019, 2020), working with Dr. Baoyuan Wu and Dr. Zhifeng Li (supported by the Tencent Rhino-bird Elite Training Program); an intern at Wukong Investment (2018), working with Dr. Xinji Liu on ML-based high-frequency trading.
I am always willing to work together on interesting projects. Feel free to drop me an email if you have any ideas or suitable opportunities to discuss!
- 03/2023: The paper about our BackdoorBox is accepted in ICLR Workshop.
- 03/2023: One paper about clean-label backdoor attack is accepted in Pattern Recognition. Its codes will be released soon.
- 02/2023: One paper about backdoor defenses is accepted in PAKDD 2023. Its codes will be released soon.
BackdoorBox: A Python Toolbox for Backdoor Attacks and Defenses
Github Repo about Backdoor Learning Resources