Areas of Specialization
Computational Geography, Human-Environment Relations, Geospatial Analysis
Dan Runfola is an assistant professor of Applied Science at William and Mary. Dan has served as PI on over $1.5 million dollars of funded research at the nexus of machine learning, imagery analysis, and climate change. His core expertise is in the applied use of machine learning to analyze spatial data - both for imagery classification and for causal attribution. In addition to 35+ peer reviewed academic publications in high profile outlets including Nature, Dan has published numerous policy-oriented reports with the US Army Corps of Engineers, Global Environment Facility, World Bank, and as a contributor to the United Nation's Intergovernmental Panel on Climate Change. At William and Mary, Dan served as the inaugural director of the Data Science Program, and is currently the PI of the Geospatial Evaluation and Observation Lab (geoLab).
- B.A., Georgia State University
- Ph.D., Clark University