Zhouxing Shi
UCLA
About me
I am a final-year Ph.D. Candidate at UCLA Computer Science Department, advised by Prof. Cho-Jui Hsieh. My primary research focus is on trustworthy machine learning, where I study formal methods for verifying and training ML models with verifiable safety guarantees in mission-critical applications, as well as empirical methods for the robustness and safety evaluation and defense for large-scale ML foundation models. I received my B.Eng. degree from the CST department at Tsinghua University.
I am currently on the job market.
Selected Publications
(* Equal contribution; for full publications: see
Publications
or
Google Scholar
)
Software
- auto_LiRPA
: A library for automatically computing differentiable verified output bounds for general computational graphs. Originally proposed in our NeurIPS 2020 paper, it is the core bound computation engine in the award-winning alpha-beta-CROWN.
- alpha-beta-CROWN
: A comprehensive neural network verification toolbox that consists of multiple complete verification algorithms (such as GenBaB) on top of auto_LiRPA. It is the 1st place winner at the annual VNN-COMP from 2021 to 2024.
Awards
- UCLA Dissertation Year Award (fellowship), 2024-2025
- Amazon Fellowship (Amazon & UCLA Science Hub fellowship), 2022-2023
- 4X first-place winner at the International Verification of Neural Networks Competition (VNN-COMP), 2021-2024
- Outstanding Bachelor’s Thesis Award, Tsinghua University, 2020
Teaching
TA at UCLA:
- CS 35L Software Construction (Fall 2023, Prof. Paul Eggert)
- CS M146 Introduction to Machine Learning (Fall 2022, Prof. Kai-Wei Chang)
- CS 35L Software Construction (Spring 2022, Prof. Paul Eggert)
- CS 260C Deep Learning (Winter 2022, Prof. Cho-Jui Hsieh)
Work Experience
- Research Scientist Intern at Meta, Sunnyvale, 2023
- Student Researcher at Google Research, New York, 2022
- Research Intern at JD AI Research, Shanghai, 2020-2021
- Research Intern at ByteDance AI Lab, Shanghai, 2020