Research. My research aims to find new ways to combine political science and computer science. The methodologies I work with strongly feature formal theory (i.e., proofs), drawing on tools from social choice, game theory, algorithms, machine learning theory, statistics, sampling methods, and survey research. I focus on applications including political methodology (particularly survey sampling and opinion measurement) and democratic innovations that facilitate more direct public participation (e.g., deliberative minipublics, participatory budgeting, and other forms of preference elicitation).
Background. Before joining MIT, I was an HDSI postdoctoral fellow at Harvard from 2024-25. I completed my PhD in computer science at Carnegie Mellon University in 2024, where I was extremely lucky to have been advised by Ariel Procaccia. My PhD was funded by a Fannie and John Hertz Foundation Fellowship and an NSF GRFP.
Before that, I studied Bioengineering at UW-Madison, where I primarily researched cancer. Between undergrad and graduate school, I spent a few years doing research in economics (Yale), computer science (Drexel), and public health (Philani Nonprofit in South Africa).