picture of me in Milan, Italy.

Daniel Alabi

alabid [at] g [dot] harvard [dot] edu

I am a Computer Science Ph.D. student at Harvard University, where I am advised by Salil Vadhan.

My research interests lie in theoretical computer science (algorithms & complexity theory) with a focus on optimization/learning with constraints. Example application domains include data privacy, algorithmic fairness, and machine learning.

Currently, my research is (mostly) supported by a Facebook Emerging Scholars Award. I spent the summer of 2018 at Microsoft Research New England mentored by Adam Kalai! In my first year at Harvard, under supervision by Cynthia Rudin and Margo Seltzer, I dabbled in interpretable ML models. Previously, I spent a year as a (non-PhD) graduate research scholar at Columbia University, working with Chris Wiggins and 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.

In my free (and not-so-free) time, I participate in ballroom activities as a member of Harvard's Competitive Ballroom Dance Team.

Research Papers

Unleashing Linear Optimizers for Group-Fair Learning and Optimization.
Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai.
COLT, July, 2018  [arxiv]  [PMLR] 
Learning Certifiably Optimal Rule Lists for Categorical Data.
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin.
JMLR, June, 2018  [arxiv] 
Long Journal Version of KDD 2017, SysML 2018 papers with more analyses/results/experiments/proofs.
Systems Optimizations for Learning Certifiably Optimal Rule Lists.
Nicholas Larus-Stone, Elaine Angelino, Daniel Alabi, Margo Seltzer, Vassilios Kaxiras, Aditya Saligrama, and Cynthia Rudin.
SysML, February, 2018  [pdf] 
Learning Certifiably Optimal Rule Lists.
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin.
KDD, August, 2017  [pdf] 
PFunk-H: Approximate Query Processing using Perceptual Models.
Daniel Alabi and Eugene Wu.
Proceedings of the International Workshop on Human-in-the-Loop Data Analytics (HILDA), June, 2016  [pdf] 
Poster at North East Database Day, January, 2016.

Selected Projects (with Code Artifacts)

Collaborative LaTeX editor.
CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space.
See my GitHub page for some more projects not listed here.


Check out some of my instructional worksheets.