Bio: I am currently a tenure-track faculty at CISPA Helmholtz Center for Information Security. Prior to that, I obtained my Ph.D. degree in the department of computer science at University of Virginia advised by Prof. David Evans in 2022. I received my M.S. degree from Department of Statistics at University of Virginia and my B.S. degree in Mathematics at Tsinghua University in 2017 and 2015, respectively. I am also a member of the European Laboratory for Learning and Intelligent Systems.
Research Interests: My research covers various topics in machine learning and security, including trustworthy machine learning, statistical machine learning, convex/non-convex optimization and deep learning. Recently, I focus on understanding the misbehavior of machine learning models against different adversaries and designing robust systems for various machine learning applications.
Open Positions: I am looking for self-motivated students who are interested in trustworthy machine learning starting in 2023, including multiple PhDs, research assistants, interns, and visiting students. Check Open Positions for more details.
Built upon on a novel definition of label uncertainty, we develop an empirical method to estimate a more realistic intirnsic robustness limit for image classification tasks.
We develop a method to measure the concentration of image benchmarks using empirical samples and show that concentration of measure does not prohibit the existence of adversarially robust classifiers.
We propose a notion of cost-sensitive robustness for measuring classifier’s performance when adversarial transformations are not equally important, and provide a certified robust training method to optimize for it.
We present a new gradient-based optimization algorithm for inductive matrix completion, which achieves both linear rate of convengence and sample complexities linearly depending on the feature dimension.