Ethan’s studies data-intensive problems in ecology including ecological forecasting and using high resolution remote sensing to understand individual level patterns in ecological systems at large scales. He is actively involved in computational training efforts as a Data Carpentry founder, The Carpentries Executive Committee member, and lead developer of the semester long Data Carpentry course.
Ethan’s research interests include the use of large datasets, machine learning, and remote sensing to understand and make forecasts for the dynamics of ecological systems at large scales.
PhD in Biology, 2004
University of New Mexico
BA in Biology, 1998