picture of me taken by Sury.

Daniel G. Alabi

Instructor, NaijaCoder
Junior Fellow, Simons Society of Fellows
Postdoctoral Researcher, Computer Science and Data Science, Columbia University
Postdoctoral Host: Daniel Hsu
alabid [at] cs [dot] columbia [dot] edu

Research Interests

Algorithms, Data Privacy, Databases & Data-Centric ML, Communication Complexity, Quantum Information

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] 
See all papers here.

Some 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.