李虓 Xiao Li

About Me

I am a Ph.D. student at EECS, University of Michigan, advised by Prof. Qing Qu. Before joining Umich, I received my undergraduate degree from the University of Washington, majoring in Mathematics, followed by a Master’s degree from NYU with a major in Data Science.
My research interests lie in representation learning, focusing particularly on the identification and effective utilization of low-dimensional structures and dynamics of deep networks. Besides, I’m also interested in robust deep learning, and Super-resolution microspcopy.

[May 2026] Paper on evaluating and monitoring diffusion model training via representation space geometry (project page) accepted at ICML 2026.
[Dec. 2025] Paper on how balanced representation space drives generalization in diffusion models (project page) accepted at ICLR 2026.
[Sep. 2025] Paper on diffusion-based representation learning dynamics accepted at NeurIPS 2025.
[Aug. 2025] Paper on layerwise representation dynamics in deep networks accepted at JMLR.
[May 2024] Paper on transfer learning and neural collapse accepted at TMLR; paper on multi-label learning and neural collapse accepted at ICML 2024.