picture of me taken by Sury.

Daniel Alabi

alabid [at] g [dot] harvard [dot] edu
Harvard University
Advised by Salil Vadhan
Ph.D. student in the Theory of Computation group and the Harvard Privacy Tools group
Photo credit: My neighbor Sury @scrodcity

In general, through the use of (mostly) mathematical tools, I study the limits and capabilities of statistics for computer science applications. My research interests are primarily in algorithmic topics related to learning theory and privacy. I am also interested in other areas in the general fields of statistics, probability, machine learning, and theoretical computer science.

Previous/Current Research Internship Experience: Microsoft Research (w/ Adam Kalai), Google Research (w/ Ravi Kumar)

Selected Research

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] 
See all papers here.

Selected Code Artifacts

Collaborative LaTeX Editor
CORELS is a custom discrete optimization technique for building decision/rule lists over a categorical feature space.
See my GitHub page for some more code artifacts not listed here.


Previous/Current Research Funding: Harvard SEAS and CRCS, Facebook/Meta AI, and the Courtlandt S. Gross Memorial Scholarship