Education

• University of Maryland, College Park, MD
  • 08/2019 - 05/2024 (expected)
  • Ph.D. in Computer Science (GPA: 4.0)
• University of Science and Technology of China, Hefei, China
  • 08/2015 - 06/2019
  • B.S. in Information Security (GPA: 3.80)

Work experience

• Salesforce, Research Intern. Palo Alto, CA
  • 06/2022 - 11/2022
  • Intern project: 3D Point Cloud Object Detection
• Nvidia, Research Intern. Santa Clara, CA (remote)
  • 01/2022 - 05/2022
  • Intern project: Zero-shot Prompt Tuning for Vision-Language Models

Research experience

• Graduate Research Assistant, University of Maryland
  • Advisor: Prof. Tom Goldstein
  • Research project: Self-supervised Representation Learning
  • Research project: Out-of-Distribution Robustness

Publications

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [paper] [code]

M. Shu , W. Nie, DA. Huang, Z. Yu, T. Goldstein, A. Anandkumar, and C. Xiao, NeurIPS, 2022

Where do models go wrong? Parameter-space saliency maps for explainability. [paper]

R. Levin^*, M. Shu^*, E. Borgnia^*, F. Huang, M. Goldblum, T. Goldstein, NeurIPS, 2022

The Close Relationship between Contrastive Learning and Meta Learning [paper] [code]

R. Ni^*, M. Shu^*, H. Souri, M. Goldblum, and T. Goldstein, ICLR, 2022

Encoding Robustness to Image Style via Adversarial Feature Perturbations [paper] [code]

M. Shu , Z. Wu, M. Goldblum, and T. Goldstein, NeurIPS, 2021

Gradient-Free Adversarial Training against Image Corruption for Learning-based Steering [paper] [code]

Y. Shen, L. Zheng, M. Shu , W. Li, T. Goldstein and M. Lin, NeurIPS, 2021

Adversarial Differentiable Data Augmentation for Autonomous Systems [paper] [code]

M. Shu , Y. Shen, M. Lin, and T. Goldstein, ICRA, 2021

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. [paper]

C. Zhu, Z. Xu, A. Shafahi, M. Shu , A. Ghiasi, and T. Goldstein, SNN (Workshop), 2021

Headless Horseman: Adversarial Attacks on Transfer Learning Models. [paper]

A. Abdelkader, M. Curry, L. Fowl, T. Goldstein, A. Schwarzschild, M. Shu , C. Studer, C. Zhu, ICASSP, 2020

[paper]