Rebecca Hutchinson

School of Electrical Engineering & Computer Science
Department of Fisheries & Wildlife


My research is at the intersection of machine learning and ecology. I am part of the computational sustainability community, trying to find ways that computer science can contribute to promoting the health of the Earth's ecosystems and bringing interesting new problems back to computer science. Much of my work is on computational methods for species distribution modeling. I work primarily with hierarchical latent variable models that represent both ecological and observation processes; for example, occupancy models and their variants fall within this paradigm. My current research is on robust parameter estimation methods for these models and techniques for incorporating semi-parametric techniques into probabilistic models. I am also interested in methods for analyzing species interaction networks and strategies for evaluating species distribution models.

Selected publications

Valente, J.J., Hutchinson, R.A. and Betts, M.G., (online early) Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models. Methods in Ecology and Evolution doi:10.1111/2041-210X.12840

Prudic, K.L., McFarland, K.P., Oliver, J.C., Hutchinson, R.A., Long, E.C., Kerr, J.T. and Larrivée, M. (2017) eButterfly: Leveraging Massive Online Citizen Science for Butterfly Conservation. Insects 8: 53.

Hutchinson, R.A., Valente, J.J., Emerson, S.C., Betts, M.G. and Dietterich, T.G. (2015) Penalized likelihood methods improve parameter estimates in occupancy models. Methods in Ecology and Evolution 6: 949–959. doi:10.1111/2041-210X.12368

Shirley, S.M., Yang, Z., Hutchinson, R.A., Alexander, J.D., McGarigal, K. and Betts, M.G. (2013) Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions. Diversity and Distributions 19: 855–866. doi:10.1111/ddi.12093

Hochachka, W.M., Fink, D., Hutchinson, R.A., Sheldon, D., Wong, W.-K. and Kelling, S. (2012) Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution 27: 130–137. doi:10.1016/j.tree.2011.11.006