Welcome! We are a team of scholars developing and applying machine learning methodology in service of ecology and conservation.

Publications

Wang, J., Hallman, T.A., Hopkins, L.M., Kilbride, J.B., Robinson, W.D., Hutchinson, R.A. (2023).
Model Evaluation for Geospatial Problems.
2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment.
(pdf)
Selected for a spotlight talk

Wang, J., Hopkins, L.M., Hallman, T.A., Robinson, W.D., Hutchinson, R.A. (2023).
Cross-validation for Geospatial Data: Estimating Generalization Performance in Geostatistical Problems.
Transactions in Machine Learning Research (TMLR), in press.
(pdf) (code)

Prudic, K.L., Zylstra E.R., Melkonoff N.A., Laura, R.E., Hutchinson, R.A. (2023).
Community scientists produce open data for understanding insects and climate change.
Current Opinion in Insect Science, in press.

Ibrahim, S., Fu, X., Hutchinson, R.A., and Seo, E. (2023).
Under-counted tensor completion with neural incorporation of attributes.
Proceedings of the 40th International Conference on Machine Learning (ICML 2023), to appear.
(arxiv)

Hopkins, L.M., Hallman, T.A., Kilbride, J., Robinson, W.D., Hutchinson, R.A. (2022).
A comparison of remotely sensed environmental predictors for avian distributions.
Landscape Ecology, 37(4), 997–1016.
(pdf) (code) (bibtex)

Roth, M., Hallman, T.A., Robinson, W.D., Hutchinson, R.A. (2021).
On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data.
ICML workshop on Tackling Climate Change with Machine Learning.
(pdf) (bibtex)
Best Paper Award (proposals track)

Barry, B.R., Moriarty, K., Green, D., Hutchinson, R.A., and Levi, T. (2021).
Integrating multi-method surveys and recovery trajectories into occupancy models.
Ecosphere, 12(12).
(bibtex)

Robinson, W.D., Hallman, T.A., and Hutchinson, R.A. (2021).
Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database.
Frontiers in Ecology and Evolution, 9.
(bioRxiv) (bibtex)

Wilson, J.K., Casajus, N., Hutchinson, R.A., McFarland, K.P., Kerr, J.T., Berteaux, D., Larrivee, M., and Prudic, K.L. (2021).
Climate change and local host availability drive the northern range boundary in the rapid expansion of a specialist insect herbivore, Papilio cresphontes.
Frontiers in Ecology and Evolution, 9.
(bibtex)

Seo, E., Hutchinson, R.A., Fu, X., Li, C., Hallman, T.A., Kilbride, J., and Robinson, W.D. (2021).
StatEcoNet: Statistical Ecology Neural Network for Species Distribution Modeling.
Proceedings of the Thirty-Fifth Conference on Artificial Intelligence (AAAI).
(pdf) (supplement) (code) (bibtex)

Fu, X., Seo, E., Clarke, J., and Hutchinson, R.A. (2021).
Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 33(8), 3117 - 3128.
(arxiv) (bibtex)

Seo, E. and Hutchinson, R.A. (2018).
Predicting Links in Plant-Pollinator Interaction Networks using Latent Factor Models with Implicit Feedback.
Proceedings of the Thirty-Second Conference on Artificial Intelligence (AAAI).
(pdf) (supplement) (bibtex)

Jones, J.A., Hutchinson, R.A., Moldenke, A.R., Pfeiffer, V.W., Helderop, E., Thomas, E., Griffin, J., and Reinholtz, A. (2018).
Landscape patterns and diversity of meadow plants and flower-visitors in a mountain landscape
Landscape Ecology, 34, 997–1014.
(bibtex)

Prudic, K.L., McFarland, K.P., Oliver, J.C., Hutchinson, R.A., Long, E.C., Kerr, J.T., and Larrivee, M. (2017).
eButterfly: Leveraging Massive Online Citizen Science for Butterfly Conservation.
Insects, 8(2).
(bibtex)

Valente, J.J., Hutchinson, R.A., and Betts, M.G. (2017).
Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models.
Methods in Ecology and Evolution, 8(12), 1707-1716.
(bibtex)

Hutchinson, R.A., He, L., and Emerson, S.C. (2017).
Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-conditional Noise.
Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI).
(pdf) (supplement) (bibtex)

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(8), 949-959.
(code in unmarked R package) (bibtex)

Yu, J., Hutchinson, R.A., and Wong, W-K. (2014).
A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists.
Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI).
(pdf) (bibtex)

Shirley, S., Yang, Z., Hutchinson, R.A., Alexander, J., McGarigal, K., and Betts, M.G. (2013).
Species distribution modeling for the people: Unclassified landsat TM imagery predicts bird distributions at fine resolutions in forested landscapes.
Diversity and Distributions, 19(7), 855-866.
(bibtex)

Hochachka, W., Fink, D., Hutchinson, R.A., Sheldon, D., Wong, W-K., and Kelling, S. (2012).
Project and Analysis Design for Broad-Scale Citizen Science.
Trends in Ecology and Evolution, 27(2), 130-137.
(bibtex)

Dietterich, T.G., Dereszynski, E., Hutchinson, R.A., and Sheldon, D. (2012).
Machine learning for computational sustainability.
2012 International Green Computing Conference (IGCC).
(pdf) (bibtex)

Hutchinson, R.A., Liu, L-P., and Dietterich, T.G. (2011).
Incorporating Boosted Regression Trees into Ecological Latent Variable Models.
Proceedings of the Twenty-fifth Conference on Artificial Intelligence (AAAI).
(pdf) (bibtex)

Yu, J., Wong, W-K., and Hutchinson, R.A. (2010).
Modeling Experts and Novices in Citizen Science data for Species Distribution Modeling.
International Conference on Data Mining (ICDM).
(pdf) (bibtex)