Daniel Alabi
Currently, my primary research interest is in the Algorithmic Foundations of Data Privacy: the study of the design, analysis, and limitations of algorithms in settings where we need to ensure individual data privacy guarantees.
In particular, I'm interested in the tradeoffs in computational and statistical resources that result when we require individual privacy guarantees and, as a consequence, the robustness of statistical models and machine learning predictors. Example computational resources include time, memory, randomness, communication, and parallelism. Example statistical resources include samples (or labeled examples for classification or regression) drawn from an unknown distribution.
Funding Acknowledgements: My research was/is supported by Harvard SEAS, Harvard CRCS, Facebook, and the Courtlandt S. Gross Memorial Scholarship.
Personal: I am Nigerian and a member of Harvard's
Selected Research
- The Cost of a Reductions Approach to Private Fair Optimization
- TPDP, November, 2019 [arxiv]
- Learning to Prune: Speeding up Repeated Computations
- with Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik
- COLT, June, 2019 [arxiv] [PMLR]
- Unleashing Linear Optimizers for Group-Fair Learning and Optimization
- with Nicole Immorlica and Adam Tauman Kalai
- COLT, July, 2018 [arxiv] [PMLR]
- Learning Certifiably Optimal Rule Lists for Categorical Data
- with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
- JMLR, June, 2018 [arxiv] [JMLR]
See all papers here.
Selected Code Artifacts
- FlyLaTeX
- Collaborative LaTeX Editor
- CORELS
- CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space.
Teaching
- 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