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 am a consulting researcher and interned during Summer 2021 and 2022 with Sébastien Bubeck and Suriya Gunasekar in the Machine Learning Foundations group at MSR Redmond.

At UW, I work on sampling algorithms for high-dimensional and ill-conditioned distributions. At MSR, I work on language models, where I currently focus on using transformers to do arithmetic operations.

Selected Publications


Non-Euclidean Sampling

Proximal Sampling

Complexity of Sampling

  • 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

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