About me

I am a final-year Ph.D. candidate in the Department of Computer Science at the University of Maryland, College Park (UMD). As a research assistant at UMD, I work with Dr. Tom Goldstein on research topics relevant to AI/ML safety.

My research interest is to develop trustworthy machine learning (AI/ML) systems, for which I have worked on model reliability, interpretability, and AI/ML safety. I have broad interests and experience in studying these problems for different ML systems, including vision-language models, large language models (LLMs), and image generative diffusion models.

During Ph.D., I have interned at Nvidia, Salesforce and Google as a research intern, where I have collaborated with many awesome professors and researchers.

Before UMD, I obtained my bachelor’s degree in information security at the University of Science and Technology of China (USTC) in June 2019.

News

  • [09/2023] One paper accepted at NeurIPS. We studied a novel vulnerability of aligned language models from the perspective of data security.
  • [11/2022] In New Orleans attending NeurIPS. Will present the work done at Nvidia about prompt tuning for vision-language models. (Excited to attend my first in-person academic conference. I wish I had printed a bigger poster.)

Selected Publications

For the complete list of publications, please refer to my google scholar page

  • On the Exploitability of Instruction Tuning
    M. Shu, J. Wang, C. Zhu, J. Geiping, C. Xiao, T. Goldstein
    to appear at NeurIPS 2023
    [Preprint] [Code]

  • On the Reliability of Watermarks for Large Language Models
    J. Kirchenbauer*, J. Geiping*, Y. Wen, M. Shu, K. Saifullah, K. Kong, K. Fernando, A. Saha, M. Goldblum, T. Goldstein
    Under review
    [Preprint] [Code]

  • Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
    M. Shu, W. Nie, D.A. Huang, Z. Yu, T. Goldstein, A. Anandkumar, C. Xiao
    NeurIPS 2022
    [Paper] [Code] [Project page]

  • Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
    R. Levin*, M. Shu*, E. Borgnia*, F. Huang, M. Goldblum, T. Goldstein
    NeurIPS 2022
    [Paper] [Code]

  • The Close Relationship Between Contrastive Learning and Meta-Learning
    R. Ni*, M. Shu*, H. Souri, M. Goldblum, T. Goldstein
    ICLR 2022
    [Paper] [Code]

  • Encoding Robustness to Image Style via Adversarial Feature Perturbation
    M. Shu, Z. Wu, M. Goldblum, T. Goldstein
    NeurIPS 2021
    [Paper] [Code]

  • Adversarial Differentiable Data Augmentation for Autonomous Systems
    M. Shu, Y. Shen, M.C. Lin, T. Goldstein
    ICRA 2021
    [Paper] [Code]

  • Model-Agnostic Hierarchical Attention for 3D Object Detection
    M. Shu, L. Xue, R. Mart'in-Mart'in, C. Xiong, T. Goldstein, J.C. Niebles, R. Xu.
    Under review
    [Preprint]

Services

Conference reviewer: NeurIPS, ICML, ICLR, CVPR, ICCV, IROS
Journal reviewer: IJCV

More about me (misc)

I enjoy doing yoga and meditation. I listen to classical music when focusing. I haven’t played many video games, but I had great times playing The Legend of Zelda (BotW) and Stardew Valley.