Simon Shaolei Du 杜少雷

Simon Shaolei Du Email: ssdu [at] cs (dot) washington (dot) edu Office: Gates 312 Google Scholar / DBLP / Talk Bio Twitter

About Me

I am an associate professor in the Paul G. Allen School of Computer Science & Engineering at University of Washington. I am also a Member of Technical Staff at XAI. My research interests are broadly in machine learning such as reinforcement learning, non-convex optimization, data synthesis and selection, and test-time compute.

Prospective students, postdocs, and visitors, please send me an email with your CV.

Selected Awards

• MIT Technology Review Innovators Under 35 Asia Pacific, 2025

• IEEE AI’s 10 to Watch, 2024

• Schmidt Sciences AI2050 Early Career Fellow, 2024

• Alfred P. Sloan Research Fellowship, 2024

• Intel Rising Star Faculty Award, 2023

• Samsung AI Researcher of the Year, 2022

• National Science Foundation CAREER Award, 2022

• AAAI New Faculty Highlights, 2021

• CMU School of Computer Science Distinguished Dissertation Award Honorable Mention, 2019

• Nvidia Pioneer Award, 2018

Selected Publications

• ThetaEvolve: Test-time Learning on Open Problems
  Yiping Wang, Shao-Rong Su, Zhiyuan Zeng, Eva Xu, Liliang Ren, Xinyu Yang, Zeyi Huang, Xuehai He, Luyao Ma, Baolin Peng, Hao Cheng, Pengcheng He, Weizhu Chen, Shuohang Wang, Simon S. Du*, Yelong Shen*

• Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures
  Mo Zhou*, Weihang Xu*, Maryam Fazel, Simon S. Du

• Optimal Multi-Distribution Learning
  Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee
  Journal of the ACM (JACM) 2025, Conference of Learning Theory (COLT) 2024

• Settling the Sample Complexity of Online Reinforcement Learning
  Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
  Journal of the ACM (JACM) 2025, Conference of Learning Theory (COLT) 2024
  

• Understanding the Acceleration Phenomenon via High-Resolution Differential Equations
  Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su
  Mathematical Programming Series A, 2022
  

• Few-Shot Learning via Learning the Representation, Provably
  Simon S. Du*, Wei Hu*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*
  International Conference on Learning Representations (ICLR) 2021
  

• Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
  Simon S. Du*, Sham M. Kakade*, Ruosong Wang*, Lin F. Yang*
  International Conference on Learning Representations (ICLR) 2020
  

• On Exact Computation with an Infinitely Wide Neural Net
  Sanjeev Arora*, Simon S. Du*, Wei Hu*, Zhiyuan Li*, Ruslan Salakhutdinov*, Ruosong Wang*
  Conference on Neural Information Processing Systems (NeurIPS) 2019
  

• Gradient Descent Finds Global Minima of Deep Neural Networks
  Simon S. Du*, Jason D. Lee*, Haochuan Li*, Liwei Wang*, Xiyu Zhai*
  International Conference on Machine Learning (ICML) 2019

• Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
  Sanjeev Arora*, Simon S. Du*, Wei Hu*, Zhiyuan Li*, Ruosong Wang*
  International Conference on Machine Learning (ICML) 2019

• Provably efficient RL with Rich Observations via Latent State Decoding
  Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
  International Conference on Machine Learning (ICML) 2019
  

• Gradient Descent Provably Optimizes Over-parameterized Neural Networks
  Simon S. Du*, Xiyu Zhai*, Barnabás Póczos, Aarti Singh
  International Conference on Learning Representations (ICLR) 2019

• Gradient Descent Can Take Exponential Time to Escape Saddle Points
  Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh
  Conference on Neural Information Processing Systems (NIPS) 2017