Ecological processes such as bird migration are complex, difficult to measure, and occur at the scale of continents, making it impossible for humans to grasp their broad-scale patterns by direct observation. However, novel data sources -- such as large sensor networks and millions of bird observations reported by human "citizen scientists" -- are providing new opportunities to understand ecological phenomena at very large scales and to improve decisions about how to conserve Earth's ecosystems. In this talk, I will describe two lines of work where novel algorithms allow us to reason about large-scale problems in ecology and conservation. First I will describe the BirdCast project, in which we are developing new algorithms to model and forecast bird migration using data from citizen scientists and weather radars. Then I will describe our work to solve conservation problems such as selecting land purchases to support the recovery of endangered species by developing new algorithms for optimization of diffusion in networks.
Bio: Daniel Sheldon is an assistant professor in the School of Computer Science at the University of Massachusetts Amherst and the Department of Computer Science at Mount Holyoke College. His research investigates fundamental problems in machine learning and applied algorithms motivated by large-scale environmental data, dynamic ecological processes, and real-world network phenomena. Previously, he was a postdoc at Oregon State University, where he held a National Science Foundation postdoctoral fellowship in Bioinformatics. Daniel received his Ph.D. in computer science from Cornell University.