I use computational social science to examine the phenomena that emerge when complex social processes and networks interact with the state, with a particular focus on authoritarian regimes. My dissertation includes papers on how authoritarians try and fail to pacify their large cities, the relationship between state decentralization and patterns of protest, and the Russian state’s attempt to control Twitter conversations surrounding the Crimean crisis. Methodologically, I use Bayesian semi-parametric models, natural language processing techniques, and time series models. I am passionate about not only keeping up with the latest advances in machine learning and Bayesian modeling, but teaching these techniques to graduate and undergraduate students.
I am an incoming post-doc at the Princeton School of Public and International Affairs, where I will be working on developing the new Institute for Research on the Information Environment.
MA in Political Science (Comparative Politics), 2018
The University of North Carolina at Chapel Hill
BA in Political Science & Russian Studies, 2016
The University of North Carolina at Greensboro
Familiar with all core algorithms
Focus on bayesian semi-parametrics
Grants and Awards