Computational ecology and environmental science; machine learning; probabilistic modeling and inference; network models; optimization.
The primary goal of Professor Sheldon's research is to develop algorithms to understand and make decisions about the environment using large data sets. He seeks to answer foundational questions (what are the general models and principles that underlie big data problems in ecology?) and also to build applications that transform large-scale data resources into scientific knowledge and policy. Some examples of his work include: spatial optimization to conserve endangered species, continent-scale modeling of bird migration, and biological interpretation of weather radar data across the US. Methodologically, Professor Sheldon's primary interests are machine learning, probabilistic inference, and network modeling. His work has contributed broadly applicable new approaches for reasoning about aggregate data in probabilistic graphical models, and for optimization of diffusion processes in networks.
Ph.D., Computer Science, Cornell University (2009); A.B., Mathematics, Dartmouth College (1999). Between 1999 and 2004, he worked at Akamai Technologies and DataPower Technology. Most recently, he was a Postdoctoral Fellow in the School of EECS at Oregon State University, where he held a National Science Foundation (NSF) Fellowship in Bioinformatics. Professor Sheldon joined the College of Information and Computer Sciences at the University of Massachusetts as an Assistant Professor in 2012. His appointment is a Five College joint faculty position shared by UMass Amherst and Mount Holyoke College.
Professor Sheldon is a co-PI on the four-year National Science Foundation grant "BirdCast: Novel Machine Learning Methods for Understanding Continent-Scale Bird Migration".