Sicheng Zhu

✉ sczhu@umd.edu Google Scholar Icon Google Scholar Twitter Icon Twitter

Sicheng Zhu

I am a fifth-year Ph.D. candidate in the Computer Science Department at the University of Maryland, College Park, advised by Prof. Furong Huang. I’ve interned at Meta GenAI and FAIR, Adobe Research, and Bosch AI center. I expect to graduate in 2025!

Research Interest

GenAI: LLM safety, reasoning, alignment, controllable generation, agent, MLLM Trustworthy machine learning: robustness, generalization, equivariance

Bio

Previously, I was a visiting scholar at the University of Virginia where I was fortunate to be advised by Prof. David Evans. I received my M.E. from Institute of Electronics, Chinese Academy of Sciences and B.S. from University of Electronic Science and Technology of China.

Publications - LLM safety and controllable generation
AdvPrefix: An Objective for Nuanced LLM Jailbreaks
Sicheng Zhu, Brandon Amos, Yuandong Tian, Chuan Guo, Ivan Evtimov
arXiv 2412.10321 [arXiv] [Code]
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
Yuancheng Xu, Udari Madhushani Sehwag, Alec Koppel, Sicheng Zhu, Bang An, Furong Huang, Sumitra Ganesh
ICLR 2025 [arXiv]
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
Bang An*, Sicheng Zhu*, Ruiyi Zhang, Michael-Andrei Panaitescu-Liess, Yuancheng Xu, Furong Huang
COLM 2024 [Website] [Code] [Dataset]
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun
COLM 2024 [arXiv] [Unofficial Code] [Media Coverage]
Publications - Copyright
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya, Zora Che, Bang An, Sicheng Zhu, Aakriti Agrawal, Furong Huang
NAACL 2025 [OpenReview]
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein, Furong Huang
NeurIPS 2024 AdvML-Frontiers Workshop [Best Paper Award]
AAAI 2025 [arXiv]
Benchmarking the Robustness of Image Watermarks
Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang
ICML 2024 [arXiv] [Website] [Code]
On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty*, Amrit Singh Bedi*, Sicheng Zhu, Bang An, Dinesh Manocha, Furong Huang
ICML 2024 [arXiv]
Publications - Generalization
More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes
Bang An*, Sicheng Zhu*, Michael-Andrei Panaitescu-Liess, Chaithanya Kumar Mummadi, Furong Huang
ICLR 2024 [arXiv] [Code]
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
Sicheng Zhu, Bang An, Furong Huang, Sanghyun Hong
ICML 2023 [Link] [Code]
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Sicheng Zhu*, Bang An*, Furong Huang
NeurIPS 2021 [Link] [arXiv] [Code]
Publications - Adversarial robustness
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu*, Xiao Zhang*, David Evans
ICML 2020 [Link] [arXiv] [Code]

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