Daniel Sheldon, University of Massachusetts Amherst and Mount Holyoke College
Thu Dec 15, 2016, 4-5pm EST
Title: Advances in Probabilistic Inference and Machine Learning for Ecosystem Monitoring
Abstract: Machine learning combined with large and novel data resources can contribute to our understanding of ecosystems in a variety of ways. This talk will describe two different applications of machine learning to ecosystem monitoring. First, I will describe our ongoing work to measure continent-scale bird migration using archived weather radar data. Machine learning algorithms automate the complex process of interpreting radar imagery and allow us to access high-level biological information in this massive data archive. Second, I will describe advances in probabilistic inference for estimating animal population parameters from survey data. We present the first exact polynomial-time inference algorithms for a class of commonly used models that include latent count variables to represent unknown population sizes. Our approach uses probability generating functions to represent and manipulate the infinite sequences that one must reason about during inference, and is much faster than existing approximate approaches.
Bio: Daniel Sheldon is an Assistant Professor of Computer Science at the University of Massachusetts Amherst and Mount Holyoke College. He received his Ph.D. from the Department of Computer Science at Cornell University in 2009, and was an NSF Postdoctoral Fellow in Bioinformatics at the School of EECS at Oregon State University from 2010-2012. His research interests are in machine learning, probabilistic modeling, and optimization applied to large-scale problems in ecology, computational sustainability, and networks. His work was recognized by a Computational Sustainability Best Paper Award at AAAI 2013, and is supported by the NSF and MassDOT.