Sinho Chewi's Website

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I am an Applied Mathematics PhD candidate at the Massachusetts Institute of Technology (MIT). My advisor is Philippe Rigollet. I received my B.S. in Engineering Mathematics and Statistics from University of California, Berkeley in 2018.

Notes

Here is a collection of notes that I have written. Click here to find the notes I took as an undergraduate at UC Berkeley. If you would like the source code for my notes, click here.

18.137

Topics in Geometric PDEs

18.1521

Introduction to Partial Differential Equations

18.158

Topics in Differential Equations

18.176

Stochastic Calculus

18.338

Eigenvalues of Random Matrices

18.966

Geometry of Manifolds II

COMPSCI 229R

Topics in Theoretical Computer Science: Interactions between Physics and Computation

Other

Research

Publications

Dimension-free log-Sobolev inequalities for mixture distributions. Hong-Bin Chen, Sinho Chewi, and Jonathan Niles-Weed. February 2021. [arXiv] [.bib]
Optimal dimension dependence of the Metropolis-adjusted Langevin algorithm. Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, and Philippe Rigollet. December 2020. [arXiv] [.bib]
Exponential ergodicity of mirror-Langevin diffusions. Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, and Austin Stromme. June 2020. [arXiv] [NeurIPS 2020] [Slides] [.bib]
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, and Philippe Rigollet. December 2020. [arXiv] [NeurIPS 2020] [Slides] [.bib]
Efficient constrained sampling via the mirror-Langevin algorithm. Kwangjun Ahn and Sinho Chewi. November 2020. [arXiv] [.bib]
Fast and smooth interpolation on Wasserstein space. Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, and Austin Stromme. October 2020. [arXiv] [.bib]
Gradient descent algorithms for Bures-Wasserstein barycenters. Sinho Chewi, Tyler Maunu, Philippe Rigollet, and Austin Stromme. July 2020. [arXiv] [COLT 2020] [COLT 2020 (Video)] [Optimal Transport: Regularization and Applications 2020 (Video)] [TGDA@OSU 2020 (Video)] [Slides] [.bib]
Matching observations to distributions: efficient estimation via sparsified Hungarian algorithm. Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, and Kannan Ramchandran. September 2019. [arXiv] [Allerton 2019] [.bib]
A combinatorial proof of a formula of Biane and Chapuy. Sinho Chewi and Venkat Anantharam. March 2018. [arXiv] [E-JC] [.bib]

Teaching

Here is a list of courses that I have taught in the past.

Summer 2018

Computer Science 70 – Discrete Mathematics & Probability Theory [co-taught with Vrettos Moulos]

I have also been a TA for these courses.

Spring 2016

Computer Science 70 – Discrete Mathematics & Probability Theory (Satish Rao, Jean Walrand)

Fall 2016

Computer Science 70 – Discrete Mathematics & Probability Theory (Sanjit Seshia, Jean Walrand)

Spring 2017

Computer Science 70 – Discrete Mathematics & Probability Theory (Satish Rao)

Electrical Engineering 126 – Probability & Random Processes (Kannan Ramchandran)

Summer 2017

Computer Science 70 – Discrete Mathematics & Probability Theory (Hongling Lu, Vrettos Moulos, Allen Tang)

Fall 2017

Computer Science 70 – Discrete Mathematics & Probability Theory (Kannan Ramchandran, Satish Rao)

Electrical Engineering 126 – Probability & Random Processes (Abhay Parekh, Jean Walrand)

Spring 2018