Lei Wu (吴磊)

Assistant Professor School of Mathematical Sciences Center for Machine Learning Research Peking University Office: 静园6院 205 Email: leiwu (at) math (dot) pku (dot) edu (dot) cn Google Scholar     Github      CV

About Me

I am currently an Assistant Professor in the School of Mathematical Sciences and Center for Machine Learning Research at Peking University.

Previously, I was a postdoc in PACM at Princeton University and in the Wharton Statistics and Data Science Department at the University of Pennsylvania. I completed my Ph.D. in computational mathematics at Peking University in 2018, advised by Prof. Weinan E. I received my B.S. degree in mathematics from Nankai University in 2012.

My research aims to understand the mechanisms behind the success of deep learning, with a particular focus on:

• The approximation and representation power of neural networks

• The dynamical behavior of popular optimization algorithms such as SGD and Adam

• Emergent phenomena in the training of large language models (LLMs)

Recruiting

We are actively seeking self-motivated postdocs, PhD students and interns to join our team. If you are interested in collaborating with me, please send an email to me with your CV and transcript as well as a brief description of your research interests.

Selected Works

• Understanding and improving LLM pre-training

  • Optimizer design: NeurIPS 2024, ICML 2025

• A stability theory of implicit regularization

  • Stability ensures SGD only select flat minima: NeurIPS 2018, NeurIPS 2022

  • Flat minima can provably generalize well: ICML 2023

  • Discovery of the Edge of Stability (EoS) phenomenon: NeurIPS 2018

• Approximation theory for machine learning

  • Kernel and random features: AoS 2025, arXiv 2024

  • Barron space theory for neural networks: CMS 2019, Contr. Appr. 2021, JMLR 2022, JML 2023, AoS 2025

  • Deep CNNs: NeurIPS 2023