Trey Billing is Principal Data Scientist at ACLED, where he applies machine learning and computational methods to the study of armed conflict and political instability. He holds a PhD in Government and Politics from the University of Maryland and previously held a postdoctoral fellowship at Ohio State's Mershon Center for International Security Studies. His research spans conflict prediction, food security, and computational social science, and has been published in outlets including the American Sociological Review, The Lancet Planetary Health, and the Journal of Conflict Resolution.
Developing forecasting models that leverage ACLED's conflict event data alongside external indicators to anticipate the onset, escalation, and spread of political violence. Work here connects machine learning methods to policy-relevant early warning.
Applying large language models and NLP techniques to the structured extraction, classification, and analysis of conflict-relevant text. Projects include dataset construction, event coding automation, and leveraging agents in the analysis of conflict dynamics.