Lei Wu (吴磊)
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Office: 静园6院 205 |
About Me
I am an Assistant Professor in the School of Mathematical Sciences and Center for Machine Learning Research at Peking University.
Previously, I was a postdoctoral researcher at PACM, Princeton University and at the Wharton School, University of Pennsylvania. I received my Ph.D. in Computational Mathematics from Peking University in 2018, advised by Prof. Weinan E, and my B.S. in Mathematics from Nankai University in 2012.
I aim to develop a predictive science of learning that bridges tractable models and real-world training, from kernel methods to neural networks and modern LLM pretraining. See Research for my research vision and selected highlights.
Recruiting
We are actively seeking self-motivated postdocs, PhD students, and undergraduate interns to join my group. If you are interested, please email me your CV, transcript, and a brief description of your background and research interests.
Recent News
2026-02: Constant-depth network with smooth activations released on arXiv.
Establishes that smooth activations (e.g., GELU, SiLU) enable smoothness adaptivity in constant-depth neural networks, achieving optimal approximation and statistical rates.
2026-02: Optimal learning-rate schedules under FSL released on arXiv.
Proves that the optimal learning-rate schedule depends on task difficulty; in the hard-task regime, the optimal schedule exhibits a warmup–stable–decay (WSD) structure.
2026-02: Fast catch-up, late switching accepted to ICLR 2026.
Studies batch-size scheduling under FSL, revealing a fast catch-up effect, which holds across linear regression and LLM pretraining.
2025-09: Functional Scaling Laws accepted to NeurIPS 2025 (Spotlight).
Introduces a functional scaling law (FSL) framework that—in contrast to classical scaling laws, which only describe final-step behavior—characterizes the entire loss trajectory, spanning from linear regression to LLM pretraining.