Daniel G. Alabi

Research Interests

Algorithms, Privacy, Databases, Quantum Computing

Short Bio

I received my Ph.D. in Computer Science from Harvard University, where I was advised by Salil Vadhan. During my program, I was a research intern at Microsoft Research in New England and the Discrete Algorithms Group at Google Research. In 2019, I received an S.M. from Harvard and before that obtained a B.A. in mathematics and a B.A. in computer science from Carleton College.

Representative Papers

Privately Estimating a Gaussian: Efficient, Robust and Optimal

with Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang

Proceedings of the ACM Symposium on Theory of Computing (STOC), 2023  [arxiv]  [ACM] 

Hypothesis Testing for Differentially Private Linear Regression

with Salil Vadhan

Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2022  [arxiv]  [NeurIPS] 

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

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