The College of William and Mary
540 Landrum Drive, Room 0279
Integrated Science Center
Williamsburg, Virginia 23185
Visit Daniel's Research Website
- Computational Geography
- Data Science
- Environmental Decision Support Systems
- Modeling and Simulation
- Remote Sensing
- Spatial Analysis
Machine Learning, Spatial Simulation, Climate Change, Decision Methods, Imagery Analysis, Neural NetworksView Daniel's CV
Dan Runfola is an assistant professor of Applied Science at William and Mary. Dan has served as PI on over $4 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).
Dan received his Ph.D. in Geography from Clark University, and conducted his postdoctoral research in a joint position between the National Center for Atmospheric Research (NCAR) and CU: Boulder.
Ongoing and Recent CESU Projects
Dan has conducted extensive research on the impact of small-scale decisionmaking (in particular, suburban lawn management strategies) on large-scale watershed processes. Most of his current work focuses on the use of neural networks and large scale imagery to detect and understand processes related to efforts to reduce deforestation (including, for example, road qualities and related human encroachment).