Ruoqi Shen

Ruoqi Shen 

Ph.D. Student
Paul G. Allen School of Computer Science & Engineering
University of Washington

Email: shenr3 [at] cs [dot] washington [dot] edu
Google Scholar


I am a PhD student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Yin Tat Lee. I received my B.S. in Computer Science from California Institute of Technology in 2018.

I interned with Sébastien Bubeck and Suriya Gunasekar in the Machine Learning Foundations group at MSR Redmond during Summer 2021 and 2022. I visited Simons Institute for the Geometric Methods in Optimization and Sampling program in Fall 2021. I visited IAS for Special Year on Optimization, Statistics, and Theoretical Machine Learning from Sep 2019 to Apr 2020.


My current research interests are in

  • Designing efficient sampling algorithms with provable guarantees and practical applications.

  • Understanding and improving fundamental algorithms in deep learning.

Selected Publications


  • The Randomized Midpoint Method for Log-Concave Sampling.
    Ruoqi Shen, Yin Tat Lee. NeurIPS 2019 (spotlight presentation).
    A sampling algorithm with the state-of-the-art guarantee on dimension dependence tilde{O}(d^{1/3}) for strongly log-concave distributions. [CLW21] shows the randomized midpoint method is an optimal discretization method.

Deep learning (theory)

  • Data Augmentation as Feature Manipulation.
    Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar. ICML 2022.
    A new perspective on data augmentation with a detailed analysis of the learning dynamic for a two-layer convolutional neural network and experimental evidence.

(α-β stands for alphabetical author order. Full publication list.)