About Me

I am a researcher and data scientist with interests in politics, conflict, development, and diplomacy.

I am currently a Lead Computational Social Scientist at Interos, a start-up that leverages big data to improve the resilience of global supply chains. My work at Interos involves developing and maintaining a range of risk-scoring models, across functional areas (ESG risk , Geopolitical risk, Operational risk) and levels of analysis (country-level, firm-level, industry-level, subnational-level).

Prior to joining Interos, I conducted researched and taught at several universities, including Baylor University in Waco, Texas; the George Washington University in Washington, D.C.; O.P. Jindal Global University in Sonipat, India; and the University of Southern California in Los Angeles, CA.

I employ a wide range of quantitative and qualitative methods in my work. On the quantitative side, I work primarily in Python and SQL on machine learning tasks, with particular expertise in model construction and Natural Language Processing. On the qualitative side, I have done fieldwork, elite interviews, participant observation, online surveys, and archival research.

I am Hungarian by origin, French by birth, American by education, and also spent formative years in Cambodia and Tanzania.  I received my B.A. from Dartmouth College in 2006, my M.A. from King’s College London in 2007, and my Ph.D. from the University of Southern California in 2016.