My research focuses on the interface between corruption, civil society, democratic backsliding, and digital media. In exploring these themes, I employ a wide variety of methods to gather and analyze data, from survey experiments and fieldwork to web scraping, big data analytics, natural language processing, and advanced regression techniques. My research portfolio encompasses several projects that explore bureaucratic politics, political favoritism in public procurement, transparency, anti-corruption campaigns, and the impact of digital media on democratic backsliding. As an original contributor to the Machine Learning for Peace Project (ML4P), I apply the latest machine learning techniques to create and analyze an unprecedented high-frequency dataset of civic space events. My colleagues and I have used that data to study everything from the effects of repressive legal changes on the content and slant of media coverage to exploring differences in reporting between international and local media across more than 60 countries.
I also have ongoing projects on crime prevention, gender-based violence, the reintegration of deported migrants and the gradient of state presence through its territory.