Daniel Alabi is a PhD student in computer science at Harvard University, affiliated with the Theory of Computation group. His current research interests are in the design and analysis of algorithms, optimization, learning theory, and computational social science. This past year, under the supervision of Cynthia Rudin and Margo Seltzer, he worked on interpretable machine learning models.
Prior to Harvard, Daniel spent a year at Columbia University’s Data Science Institute and Applied Mathematics Department, working with Chris Wiggins and Eugene Wu. He obtained his bachelor’s degrees in Mathematics and Computer Science from Carleton College where he was a Kellogg International Scholar.