I'm a Visiting Fellow at the Carnegie Endowment for International Peace, where I work on policy for AI and algorithms. Before joining Carnegie, I completed my Ph.D. in the Center for Machine Learning at Georgia Tech, where I was supported by the NDSEG Fellowship.
I'm interested in the impacts of AI on inequality and democracy. My current work explores value-interest tensions in calls for AI with "democratic values," using case studies on AI explainability and human rights impacts. My prior policy research has explored public opinion on AI adoption and governance, societal impacts of machine-learning-enabled disinformation, and governance strategies for neurotechnologies. I'm also vice chair of IEEE-USA's AI policy committee.
My Ph.D. research developed mathematical tools that use structure hidden in data to help scientists understand complex systems. My work focused on causality and low-dimensional structure, including (1) theory and algorithms for causal inference in dynamical systems; (2) causality-inspired methods for explaining black-box classifiers; and (3) Bayesian methods for exploiting temporal and low-dimensional structure in inverse problems.