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Zhanhao Hu 胡展豪

Postdoc at UC Berkeley

Contact:
zhanhaohu[DOT]cs[AT]gmail[DOT]com
Google Scholar Github

Affiliation:
Department of Electrical Engineering and Computer Sciences (EECS),
Institute for Data Science (BIDS),
UC Berkeley, California, 94720


I am a postdoc in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, advised by Prof. David Wagner. I received my Ph.D. in Computer Science and Technology from Tsinghua University in 2023, advised by Prof. Bo Zhang and Prof. Xiaolin Hu. I was also honored to work with Prof. Jun Zhu and Prof. Jianming Li. I received my Bachelor’s degree in Mathematics and Physics from Tsinghua University in 2017.

My research interests include robustness/safety/security issues in deep learning, especially in Computer Vision (CV) and Large Language Models (LLMs). My current focuses are adversarial examples, jailbreaking, and prompt injection.

My view is that robustness is a necessary condition for Artificial General Intelligence (AGI). It provides a perspective on understanding whether and how a paradigm can lead to AGI. It is a good perspective because people in this area consider boundaries of deep learning algorithms instead of evaluating them statically. Think about this: If an AI cannot understand “safety” and cannot reliably follow instructions, how can you believe it is able to understand your work?

I'm in the job market this year.

Special thanks to Kexin for taking the profile picture.


Selected

  1. ICLR
    GradShield: Alignment Preserving Finetuning
    Zhanhao Hu*, Xiao Huang*, Patrick Mendoza, Emad Alghamdi, Basel Alomair, Raluca Ada Popa, and David Wagner
    Accepted by ICLR, 2026
    2025GradShield.jpg
  2. ICLR
    JULI: Jailbreak Large Language Models by Self-Introspection
    Jesson Wang*, Zhanhao Hu*, and David Wagner
    Accepted by ICLR, 2026
    2025juli.jpg
  3. Neurips
    Spotlight
    Toxicity Detection for Free
    Zhanhao Hu, Julien Piet, Geng Zhao, Jiantao Jiao, and David Wagner
    In The Thirty-Eighth Annual Conference on Neural Information Processing Systems (Neurips), 2024
    2024toxicity.jpg
  4. CVPR
    Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
    Zhanhao Hu*, Wenda Chu*, Xiaopei Zhu, Hui Zhang, Bo Zhang, and Xiaolin Hu
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
    2023natural.jpg
  5. CVPR
    Oral
    Adversarial Texture for Fooling Person Detectors in the Physical World
    Zhanhao Hu, Siyuan Huang, Xiaopei Zhu, Fuchun Sun, Bo Zhang, and Xiaolin Hu
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
    2022texture.jpg
  6. CVPR
    Oral
    Infrared Invisible Clothing: Hiding from Infrared Detectors at Multiple Angles in Real World
    Xiaopei Zhu, Zhanhao Hu, Siyuan Huang, Jianmin Li, and Xiaolin Hu
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
    2022infrared.jpg