Yiwei Chen (陈一苇)

I am a first-year CS Ph.D. student focusing on Trustworthy ML and Scalable AI at Michigan State University (MSU), where my advisor is Prof. Sijia Liu. Before that, I earned both my Master's degree and Bachelor's degree from Xi’an Jiaotong University (XJTU). Also I was admitted to the Young Gifted Program at XJTU.

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My research interests lie at the intersection of robust and explainable artifical intelligence with the long-term goal of making AI systems safe and scalable. Currently, I'm working on:

• Post-training of LLM, MLLM
• Trustworthy algorithms on LLM, MLLM
• Machine Unlearning, Safety Alignment

• 2025-05 Unlearn Detection focusing on the detecting unlearning traces in LLM from model outputs!
• 2025-03 Safety Mirage focusing on the application of machine unlearning on VLM safety alignment, which is the first project during my PhD journey!
• 2024-08 Started PhD journey at Michigan State University!
• 2024-06 Graduated from Xi'an Jiaotong University!

Large language models retain persistent fingerprints in their outputs and hidden activations even after unlearning. These traces enable detection of whether a model has been “unlearned,” exposing a new vulnerability in reverse-engineering forgotten information.

Conventional supervised safety fine-tuning of VLMs suffers from the “safety mirage” problem due to training data bias, resulting in spurious correlations and over-rejections following one- word attacks. Employing unlearning algorithms on VLMs effectively removes harmful content and addresses these safety issues.

Propose a tile classification based viewport prediction method with Multi-modal Fusion Transformer to improve the robustness of viewport prediction.