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
Recent Themes in Resource Tradeoffs: Privacy, Fairness, and Robustnessworkshop at the University of Minnesota in June 2019
See all papers here.