Sinho Chewi's Website

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I am an Applied Mathematics PhD candidate at the Massachusetts Institute of Technology (MIT), advised by Philippe Rigollet. I received my B.S. in Engineering Mathematics and Statistics from University of California, Berkeley in 2018. In Fall 2021, I participated in the Simons Institute program on Geometric Methods in Optimization and Sampling and co-organized (with Kevin Tian) a working group on the complexity of sampling. In Spring 2022, I visited Jonathan Niles-Weed at New York University (NYU). In Summer 2022, I was a research intern at Microsoft Research, supervised by Sébastien Bubeck and Adil Salim.

Book








I am currently writing a book on the complexity of log-concave sampling. You can read the current draft here.

Any feedback is appreciated!

Last Updated: September 25, 2022

Notes

Click here to find the notes I took during my undergraduate and graduate studies.

Research

Publications

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)
    Summer 2017
    Computer Science 70 – Discrete Mathematics & Probability Theory (Hongling Lu, Vrettos Moulos, Allen Tang)

I have led some Directed Reading Programs (DRP): 2019, 2020, 2021(A), 2021(B).

In 2016-2017, I won the EECS Outstanding GSI/Distinguished GSI Award.

Contact

You can email me at schewi@mit.edu.


2-390D (Mathematics)

E17-476G (IDSS)

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