picture of me in Milan, Italy

Daniel Alabi

alabid [at] g [dot] harvard [dot] edu
1st year Ph.D. student at Harvard University.

Currently, my research interests are in optimization and computational learning theory.

Previously, I spent a year as a graduate research assistant at Columbia University, working with Prof. Chris Wiggins and Prof. Eugene Wu. Before coming back to academia, I was a Software Engineer on the distributed systems team at MongoDB. I obtained my bachelor's degrees in Mathematics and Computer Science from Carleton College where I was a Kellogg International Scholar.

Selected Projects

See my GitHub page for projects not listed here.
CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space.
My research was on the use of human perceptual models to make interactive visualizations faster by leveraging approximation (with formal guarantees).
Collaborative LaTeX editor.


Learning Certifiably Optimal Rule Lists for Categorical Data.
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin.
In KDD, August, 2017  [arxiv]  [bib]
Selected for oral presentation (8.5% acceptance rate)
PFunk-H: Approximate Query Processing using Perceptual Models
Daniel Alabi and Eugene Wu.
In Proceedings of the International Workshop on Human-in-the-Loop Data Analytics (HILDA), June, 2016  [pdf]  [bib]
Poster at North East Database Day, January, 2016.
Selected for full oral presentation at HILDA 2016


Check out some of my instructional worksheets.