Wikle, C.K. and A. Zammit-Mangion, 2023: Statistical deep learning for spatial and spatiotemporal data. Annual Review Statistics and its Application. 10:247-270.  link to paper

Yoo, M. and C.K. Wikle, 2023: Using Echo State Networks to Inform Physical Models for Fire Front Propagation. Spatial Statistics. 54, https://doi.org/10.1016/j.spasta.2023.100732. link to paper

Daw,R. and C.K. Wikle, 2023: REDS: Random ensemble deep spatial prediction. Environmetrics,  DOI:10.1002/env.2780 link to paper

Wikle, C.K., Datta, A., Hari, B.V., Boone, E.L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S.J., Burr, W.S., and W. Chang, 2022: An Illustration of Model Agnostic Explainability Methods Applied to Environmental Data. Environmetrics. DOI:10.1002/env.2772 link to paper

Schafer, T.L.J., Wikle, C.K., and M.B. Hooten, 2022: Bayesian inverse reinforcement learning for collective animal movement. Annals of Applied Statistics, 16(2): 999-1013 link to paper

Daw, R., Simpson, M., Wikle, C.K., Holan, S.H., and J.R. Bradley, 2022: An overview of univariate and multivariate Karhunen Lo`eve expansions in statistics. Journal of the Indian Society for Probability and Statistics, https://doi.org/10.1007/s41096-022-00122-9. link to paper

Gopalan, G., and C.K. Wikle, 2022: A multi-surrogate higher-order singular value decomposition tensor emulator for spatio-temporal simulators. Journal of Agricultural, Biological, and Environmental Statistics, 27: 22–45. https://doi.org/10.1007/s13253-021-00459-x.  link to paper

North, J.S., Wikle, C.K., and E.M. Schliep, 2022: A Bayesian approach for data-driven dynamic equation discovery. Journal of Agricultural, Biological, and Ecological Statistics, https://doi.org/10.1007/s13253-022-00514-1.  link to paper

Zammit-Mangion, A. and Wikle, C.K., 2020. Deep integro-difference equation models for spatio-temporal forecasting. Spatial Statistics, 37, https://doi.org/10.1016/j.spasta.2020.100408. link to paper

Bradley, J.R., Holan, S.H., and Wikle, C.K., 2020. Bayesian hierarchical models with conjugate full- conditional distributions for dependent data from the natural exponential family. Journal of the American Statistical Association, 115:532, 2037-2052, DOI: 10.1080/01621459.2019.1677471. link to paper

Bradley, J.R., Wikle, C.K., and S.H. Holan, 2020. Hierarchical models for spatial data with errors that are correlated with the latent process. Statistica Sinica, 30, 1–41, doi:10.5705/ss.202016.0230. link to paper

Wikle, C.K., 2019: Comparison of deep neural networks and deep hierarchical models for spatio-temporal data. Journal of Agricultural, Biological and Environmental Statistics, 24, 175–203; https://doi.org/10.1007/s13253- 019-00361-7  link to paper