Lingzhi Yuan
University of Maryland, College Park, Department of Computer Science.
 
 lingzhiyxp [at] gmail.com
lingzhiy [at] umd.edu
College Park, MD
I’m currently a first-year PhD Student in Computer Science at University of Maryland, College Park, advised by Professor Furong Huang. Before that, I obtained my BEng degree at Zhejiang University majoring in Automation. I was also a research assistant at University of Chicago, Secure Learning Lab advised by Professor Bo Li.
During my undergraduate studies, I was also a member of Intensive Training Honors Program of Innovation and Entrepreneurship (ITP) at Chu Kochen Honors College.
My current research concentrates on Trustworthy Machine Learning, with a particular emphasis on enhancing the safety and robustness of advanced models. My research centers on exploring the vulnerability of cutting-edge ML models and developing reliable defense mechanisms to safeguard their widespread deployments. By tackling these challenges, I aim to contribute to the development of AI technologies that are not only high-performing but also secure, transparent, and aligned with ethical standards.
You could refer to my resume for more detail.
Find other interesting things about me in this page🧩!
news
| Aug 20, 2025 | I become a PhD student at University of Maryland, advised by Prof. Furong Huang! | 
|---|---|
| Jan 23, 2025 | A paper accepted to ICLR 2025. | 
| Nov 15, 2024 | A paper submitted for CVPR 2025 review. | 
| Oct 02, 2024 | A paper submitted for ICLR 2025 review. | 
| Mar 15, 2024 | I became a research internship student at Secure Learning Lab, University of Chicago, supervised by Prof. Bo Li. | 
selected publications
-   PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models2025Arxiv Pre-print PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models2025Arxiv Pre-print
-   MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models2025ICLR 2025 MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models2025ICLR 2025