My research interests include machine learning algorithms, combinatorial optimization, differential privacy, and fairness in machine learning. I am grateful to be advised by Mohit Singh. Our current research has been finding better (randomized and deterministic) polynomial-time approximation algorithms for optimal design problems in statistics. This line of work is joint with Aleksandar (Sasho) Nikolov and Vivek Madan.
In addition, I am working on differentially privacy. The first project is on growing databases with Rachel Cummings and Sara Krehbiel. Part of the work was presented at TPDP2017. Rachel and I also is a part of the team winning first prize and people's choice award ($20000 total) for NIST's privacy challenge . Our proposed solution is by differentially private generation of synthetic data via GANs and is presented at TPDP2018.
My recent direction of work is fairness in machine learning. In particular, my coauthors and I defined a notion of fairness in PCA and proposed an algorithm (code on GitHub) that has theoretical guarantee and practically useful. The work appeared at NeurIPS 2018 in Montreal, Canada.
As an undergraduate I studied mathematics at University of Richmond. My undergraduate research was primarily in discrete geometry and algebraic combinatorics, including bent functions (coding theory) and partial different sets.
My undergraduate thesis, under the supervision of James Davis, was in the area of algebraic combinatorics, coding theory, and discrete geometry, specifically on Cameron-Liebler line classes and partial difference sets, which includes a new non-existence result of partial difference sets in a certain class of abelian groups.I am originally from Bangkok, Thailand. I graduated high school from Bangkok Christian College. During middle and high school period, I was involved in and very much enjoyed national and international mathematics competitions and many serious training that came with those.