Dear all, Our next AI seminar is scheduled to be on February 2nd (Today), 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/96491555190?pwd=azJHSXZ0TFQwTFFJdkZCWFhnTW04UT09<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fj%2F96491555190%3Fpwd%3DazJHSXZ0TFQwTFFJdkZCWFhnTW04UT09&data=05%7C02%7Cai%40engr.orst.edu%7C4162f1ec6c4b4f0e97aa08dc241088cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638424900659949735%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Itd0SlDltqGwUKttKh4GD1enWsVH0h9n%2FlOUo%2BBF5qo%3D&reserved=0> Cross-validation for Geospatial Problems 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 Bio: 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 Ph.D. at Carnegie Mellon University with Tom Mitchell. She is the recipient of an NSF CAREER Award. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI&data=05%7C02%7Cai%40engr.orst.edu%7C4162f1ec6c4b4f0e97aa08dc241088cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638424900659949735%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=yYBnLszW6qb6zr1hTlBBBtIhIm9zB2YaVT9XpGddY7M%3D&reserved=0> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged