About Me

• Hi! I am a second-year Ph.D. student at EECS, University of Michigan, advised by Prof. Qing Qu. Before joining U-M, I received my undergraduate and master’s degrees from the Wuhan University, majoring in software engineering and computer science, respectively, advised by Prof. Weiwei Liu.

• My research interests lie in diffusion models and vision-language models, with a particular focus on model collapse and representation learning. I prefer to use mathematical tools to design algorithms and explain intriguing phenomena observed in experiments, providing insights for further algorithm development.

Recent News

• 12/2/2025: I will be attending NeurIPS 2025. See you there.
• 9/26/2025: Passing the qualifying exam!
• 9/18/2025: One paper accepted by NeurIPS 2025 as a spotlight paper.

Publications

A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
Lianghe Shi*, Meng Wu*, Huijie Zhang, Zekai Zhang, Molei Tao, Qing Qu.
ICML 2025 Workshop (Oral) and NeurIPS 2025 (Spotlight).
Website

Adversarially robust unsupervised domain adaptation
Lianghe Shi*, Weiwei Liu. The journal of Artificial Intelligence. AIJ.

A Closer Look at Curriculum Adversarial Training: From an Online Perspective
Lianghe Shi*, Weiwei Liu. Proceedings of the 38th AAAI Conference on Artificial Intelligence. AAAI 2024.

Adversarial self-training improves robustness and generalization for gradual domain adaptation
Lianghe Shi*, Weiwei Liu. 37th Conference on Neural Information Processing Systems. NeurIPS 2023.

Teaching

• Graduate Student Instructor of EECS 501: Probability and Random Processes (Fall 2025)

Academic Services

• Conference Reviewer: NeurIPS, ICML, ICLR, AISTATS.