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 deep learning by connecting insights from tractable surrogate models with real-world training, spanning kernel methods, 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 students interested in the scientific foundations of deep learning, particularly approximation, optimization, scaling laws, and LLM pretraining.
If you are interested in joining our group, please email me your CV, transcript (for student applicants), and a brief description of your background and research interests.
Recent Updates
2026-07: I will teach a short course on Foundations of Deep Learning: Approximation, Generalization, and Scaling at 2026 Fudan University International Summer School on AI in Math.
2026-07: Co-organized the 2026 PKU Workshop on Mathematical Foundations of Modern Machine Learning with Fanghui Liu and Kun Yuan.
2026-06: Optimal Learning-Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay accepted to COLT 2026.
2026-06: GradPower: Powering Gradients for Faster Language Model Pre-Training accepted to ICML 2026.
2026-02: Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations released on arXiv.
2026-02: Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws accepted to ICLR 2026.
2025-09: Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules accepted to NeurIPS 2025 (Spotlight).