Research Matthew Heaton Home Research Teaching Contact Vita

For a complete list of publications, please see my CV and Google Scholar page.

I am happy to share any of my publications and/or code if you contact me.

About My Research

My research focuses on scalable statistical and machine-learning methods for spatial and spatiotemporal data, with emphasis on uncertainty quantification and real-world decision making. I develop methods that integrate spatial dependence into Bayesian models and modern machine-learning tools, including Gaussian processes, Bayesian regression trees, neural networks, and point process models. This work is motivated by applications in environmental science, climate and hydrology, public health, transportation safety, and precision agriculture, and has been supported by funding from agencies such as NSF, NASA, NIH, and FHWA. A central component of my research program is mentoring undergraduate and graduate students, who frequently serve as research collaborators and coauthors.

Most Recent Publications

Heaton, M.J., Miller, K.M. and Rhodes, J.S. (2026). “Continuous Crash Risk Estimation from Discrete, Multivariate Crash Count Data,” Journal of the Royal Statistical Society: Series C.

Heaton, M.J., Millane, A. and Rhodes, J. (2025). “A Scalable Spatial Decorrelation Preprocessing Approach for Machine and Deep Learning,” Journal of Data Science.

Dahl, B., Heaton, M.J., Fisher, J. and Warr, R. (2025). “Modeling Crash Risk on Roadway Networks using Bayesian Regression Trees,” Technometrics 67(2), 225–237.

Heaton, M.J. and Johnson, J. (2024). “Minibatch Markov Chain Monte Carlo Algorithms for Fitting Gaussian Processes,” Bayesian Analysis.

Johnson, J., Heaton, M.J., Christensen, W.F., Warr, L. and Rupper, S. (2024). “Climate Data Melding using a Spatially-varying Autoencoder,” Journal of Agricultural, Biological and Environmental Statistics.

Heaton, M.J., Dahl, B., Dayley, C., Warr, R. and White, P. (2024). “Integrating Machine Learning and Bayesian Nonparametrics for Flexible Modeling of Spatial Point Patterns,” Computational Statistics and Data Analysis 191, 107875.