Daniel G. Alabi
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.
Research Overview
In general, I study the limits and capabilities of statistics for computer science applications. In particular, I'm interested in the tradeoffs in computational and statistical resources that result when we require provable guarantees (e.g., for privacy) on 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.
Selected Papers
- Privately Estimating a Gaussian: Efficient, Robust and Optimal
- with Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang
- To appear: Proceedings of the ACM Symposium on Theory of Computing (STOC), 2023 [arxiv]
- 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]
Selected Code Artifacts
- FlyLaTeX
- Collaborative LaTeX Editor
- CORELS
- CORELS is a custom discrete optimization technique for building decision/rule lists over a categorical feature space.
Teaching
- 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