Research Overview
In general, through the use of (mostly) mathematical tools, I study the limits and capabilities of statistics
for computer science applications.
Currently, I am particularly interested in understanding the limits of performing computational social science under different
constraints. e.g., for learning with limited resources or with privacy.
I am also interested in
other areas in the general fields of statistics, probability, machine learning, and theoretical computer science.
Selected Papers
 Differentially Private Simple Linear Regression
 with Audra McMillan, Jayshree Sarathy, Adam Smith, and Salil Vadhan

Proceedings of the Journal on Privacy Enhancing Technologies (PoPETs), 2022
[arxiv]
[PETS]
 Learning to Prune: Speeding up Repeated Computations
 with Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik

Proceedings of the Conference on Learning Theory (COLT), 2019
[arxiv]
[PMLR]
 Unleashing Linear Optimizers for GroupFair Learning and Optimization
 with Nicole Immorlica and Adam Tauman Kalai

Proceedings of the Conference on Learning Theory (COLT), 2018
[arxiv]
[PMLR]
 Learning Certifiably Optimal Rule Lists for Categorical Data
 with Elaine Angelino, Nicholas LarusStone, Margo Seltzer, and Cynthia Rudin

Proceedings of the Journal of Machine Learning Research (JMLR), 2018
[arxiv]
[JMLR]
See all papers here.