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. Yet we urgently need to improve scientific understanding and design conservation practices help protect Earth's ecosystems from threats such as climate change and human development. Fortunately, novel data sources---such as large sensor networks and millions of bird observations reported by human "citizen scientists"---provide new opportunities to understand ecological phenomena at very large scales. The ability to fit models, test hypotheses, make predictions, and reason about human impacts on biological processes at this scale has potential to revolutionze ecology and conservation.
In this talk, I will present work from two broad algorithmic frameworks designed to overcome challenges in model fitting and decision-making in large-scale ecological science. Collective graphical models permit very efficient reasoning about probabilistic models of large populations when only aggregate data is available; they apply to learn about bird migration from citizen-science data and also to learn about human mobility from data that is aggregated for privacy. Stochastic network design is a framework for designing robust networks and optimizing cascading behavior in networks; it applies to spatial conservation planning, optimizing dam removal in river networks, and increasing the resilience of road networks to natural disasters.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 2016, and is supported by the NSF and MassDOT.