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RESEARCH

I am broadly interested in theoretical computer science and applied mathematics. In particular, I am interested in the intersection of continuous and discrete optimization, discrepancy theory, high dimensional probability, and their applications. Here are some recent (specific) directions: 

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  • Submodular Function Minimization. In a sequence of work, e.g. [J.2022], [CGJS2022], [GJS2023], we aim at understanding the best amount of evaluation queries needed to minimize a general submodular function. 

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  • Discrepancy Theory and Applications. Discrepancy theory studies the irregularity of distributions, or how well discrete solutions approximate continuous ones. Recently, we made progress towards the matrix Spencer conjecture [DJS2022][BJM2023], and use discrepancy as tools for designing better quasi-Monte Carlo methods [BJ24].  

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  • Matrix/Tensor Concentration Inequalities and Applications. In this paper above [BJM2023], our result was achieved via an application of a recent sharp concentration inequality of [BBvH'23]. In an upcoming work, we gave the first non-trivial tensor concentration inequalities and several applications, e.g. tensor PCA. 

TEACHING

Courses at UChicago

  • CMSC/MATH 39600-1: Geometric Discrepancy Theory (Autumn 2024)

  • CMSC 31801-1: High-Dimensional Probability with Applications in Data Science (Autumn 2024)

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I've also been a TA for the following courses at UW:

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  • CSE 599M Robustness in Machine Learning, Fall 2019

  • CSE 599Q Intro to Quantum Computing, Winter 2022

Theory Group

Department of Computer Science

University of Chicago

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