I like to think about game theory, learning theory, algorithmic fairness, and computational social science. I like to think about how stragegic agents, algorithmic processes, and social norms come together and interact, both mathematically and practically.
My recent work has focused on using algorithmic frameworks to offer insight into non-computational settings, including using examining how self-interested agents might lie about their information to protect their privacy, considering how algorithmic approaches to drawing fair districts might fall short, and solving computational geometry problems inspired by issues in shape analysis for redistricting.
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 am also a research associate 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 The Gerrymandering Jumble: Map Projections Permute Districts’ Compactness Scores will appear in Cartography and Geoographic Information Science!
Our paper Geometry of Graph Partitions via Optimal Transport is on the arXiv!