Daniel G. Alabi

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 Group-Fair 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 Larus-Stone, Margo Seltzer, and Cynthia Rudin

Proceedings of the Journal of Machine Learning Research (JMLR), 2018  [arxiv]  [JMLR] 

• Teaching Fellow, Applied Privacy for Data Science (CS 208), Harvard, Spring 2022
• Teaching Fellow, Algorithms at the End of the Wire (CS 222), Harvard, Fall 2020
• Teaching Fellow, Algorithms & Data Structures (CS 124), Harvard, Spring 2018
• Teaching Assistant, Data Structures (CS 201), Carleton College, Winter & Spring 2014
• Teaching Assistant, Intro to Computer Science (CS 111), Carleton College, Fall 2013