Algorithms for Large-Scale Ecology and Environmental Policy
When: Monday, January 23, 2012 - 4:00pm - 4:50pm
Where: KEC 1001
Speaker Information
Speaker Title/Description:
Post-Doctoral Researcher
School of EECS
Oregon State University
Speaker Biography:
Daniel Sheldon is a postdoctoral fellow in the School of EECS at Oregon State University, where he holds an NSF fellowship in Bioinformatics. His primary research interests are machine learning and probabilistic modeling applied to large-scale problems in ecology and computational sustainability. Other research interests include web search and reputation systems, optimization, statistics, and network modeling. He completed his Ph.D. in computer science at Cornell University in 2009. Prior to that, he received an A.B. in mathematics from Dartmouth College in 1999, and worked at Akamai Technologies and then DataPower Technology between 1999 and 2004.
Ecological processes such as bird migration are complex, difficult to observe, and occur
at the scale of continents, making it impossible for humans to grasp their broad-scale
patterns directly. 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. The
ability to fit models, test hypotheses, make predictions, and reason about human impacts
on biological processes at this scale promise to revolutionize ecological science and
environmental policy.
In this talk, I will present novel algorithmic approaches to overcome challenges
throughout the "pipeline" from low-level data interpretation to model fitting
to high-level decision-making in large-scale ecological science, including: (1)
biological interpretation of NEXRAD weather radar, (2) probabilistic modeling of bird
migration using citizen science data and (3) optimizing land purchases to support the
recovery of endangered species. I will highlight contributions from this work that extend
well beyond ecology, including a very general optimization framework for maximizing the
spread of a cascading process in a network, and a formalism called Collective Graphical
Models for efficiently reasoning about probabilistic models of large populations of
individuals when only aggregate data is available.
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