Welcome! I am a Ph.D. candidate at New York University’s Politics Department. My substantive specialization is on the comparative politics of authoritarian regimes with a focus on Chinese politics. My methodological focus lies in applying text as data and machine learning tools towards understanding the behavior of regimes, media, and their citizenry.

In my first dissertation paper, I test whether Chinese state media engages in a strategy of “negative legitimation” by portraying the politics of liberal democracies as being chaotic. Using a novel method of measuring propaganda, I find robust evidence that is consistent with a negative legitimation strategy. My second dissertation paper expands on this result, finding that China’s state media targets executive elections in liberal democracies for unfavorable coverage.

One challenge I faced in my other dissertation projects was identifying Chinese-language semantically-related dictionaries. I address this need in my third dissertation project, where I present conclust, an algorithm based on word embeddings, that enables researchers to derive semantically related keywords using only 3-5 seed words. In addition to this paper, I have contributed to making neural network machine learning models accessible to a broader audience by writing functions for the quanteda.classifiers package.

Download my resumé.