Jingyang Zhang
Research Scientist @ Virtue AI | Ph.D. @ Duke | B.Eng. @ Tsinghua
I’m currently a Research Scientist at Virtue AI, focusing on LLM safety (redteaming, guardrail, etc.). Previously, I got my Ph.D. from Duke ECE in 2024 where I have the honor to be advised by Prof. Yiran Chen and Prof. Hai (Helen) Li. I obtained my Bachelor degree in 2019 from the Dept. of Electronic Engineering at Tsinghua University. My dissertation research topics include adversarial machine learning and distributional shifts (specifically out-of-distribution detection). I have interned at Bosch Center for AI and Tesla over the summers.
I’m a big fan of open-source projects. Notably, I’m one of the main contributors to OpenOOD, the largest (and arguably the most well-recognized) codebase for out-of-distribution detection (on image data). I also develop lmms-finetune, a lightweight framework for fine-tuning a variety of vision LLMs on custom data, and VLM-Visualizer, an attention-based visualization that studies which part of the image the LLM looks at when predicting each token.
I (try to) write technical blogs from time to time. You can find them on Medium and 知乎.
news
| Aug 04, 2025 | I’m happy to join Virtue AI as a Research Scientist to work on advancing LLM safety. |
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| Jan 22, 2025 | Excited to share that our work Min-K%++ (a novel method for pretraining data detection) has been accepted to ICLR2025 as Spotlight! See [html][code][arxiv] for more details. |
| Jan 06, 2025 | I start my job as a Machine Learning Engineer at Sciforium today. As one of the founding team members, I will be developing the latest multi-modal LLMs and touching almost every aspect of the whole spectrum, including data curation, model development, training, and deployment. |
| Dec 15, 2023 | Excited to present our work OpenOOD v1.5 as an oral presentation at NeurIPS DistShift workshop! See [html][code][arxiv] for more details. |
| May 13, 2023 | I’m joining Tesla as a Machine Learning Intern this summer. |
