Rebecca Hutchinson, Associate Professor
School of Electrical Engineering and Computer Science
Department of Fisheries, Wildlife, and Conservation Sciences
Oregon State University

Abstract
Most machine learning practitioners have used cross-validation to assess the generalization performance of a model. However, typical cross-validation methods assume that the data are independent and identically distributed (iid). In geospatial problems, data points have geolocated coordinates (like latitude and longitude) as well as associated features, which are often spatially autocorrelated. Furthermore, geospatial problems may entail prediction to new regions, where the features are distributed differently. These properties violate the iid assumption. With a motivating ecological example of evaluating species distribution models, this talk will address theoretical and practical challenges that arise when conducting cross-validation for geospatial applications.

Speaker Biography
Rebecca Hutchinson is an Associate Professor at Oregon State University, with a joint appointment across the School of Electrical Engineering and Computer Science and the Department of Fisheries, Wildlife, and Conservation Sciences. She is also affiliated with the Center for Quantitative Life Sciences and the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. She became interested in interdisciplinary work in machine learning and quantitative ecology during her postdoctoral studies with the Institute for Computational Sustainability and as an NSF SEES Fellow, advised by Tom Dietterich and Matt Betts. Prior to that, she completed her PhD at Carnegie Mellon University with Tom Mitchell. She is a recipient of an NSF CAREER Award.