I like to think about game theory, learning theory, algorithmic fairness, and computational social science. I like to think about how strategic agents, algorithmic processes, and social norms come together and interact, both mathematically and practically.
My current work examines social considerations of algorithmic outputs in a variety of contexts, including how differentially private census statistics might offer different levels of privacy protection for members of different demographic groups, the design of consumer financial products which can fairly serve customers with diverse risk tolerances, and the effects of using automated procedures to draw electoral districts on the representativeness of the elected slate of candidates.
At Penn, I am affiliated with the CS Theory Research Group, the Warren Center for Network & Data Sciences, and the Penn Research in Machine Learning (PRiML) group. I have also worked with the Metric Geometry and Gerrymandering Group at MIT and Tufts and was on the faculty for their 2019 Voting Rights Data Institute summer program.
Our paper Algorithms and Learning for Fair Portfolio Design is on the ArXiv!
Our paper The Gerrymandering Jumble: Map Projections Permute Districts’ Compactness Scores will appear in Cartography and Geoographic Information Science!