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.
- 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
- 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
- Unleashing Linear Optimizers for Group-Fair Learning and Optimization
- with Nicole Immorlica and Adam Tauman Kalai
Proceedings of the Conference on Learning Theory (COLT), 2018
See all papers here.
- Learning Certifiably Optimal Rule Lists for Categorical Data
- with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
Proceedings of the Journal of Machine Learning Research (JMLR), 2018