Zhang, L., Bhaganagar, K., and C.K. Wikle, 2026: Capturing extreme events in turbulence using an extreme variational autoencoder (XVAE). Neural Computing and Applications. link to paper

Wang, S., Wikle, C.K., Micheas, A.C., Mark Welch, J.L., Starr, J.R.., and K.H. Lee, 2026: Inference for stationary log-Gaussian Cox point processes using Bayesian deep learning: application to human oral microbiome image data. Spatial Statistics. 73, 100973. link to paper

Wikle, C.K., North, J.S., Gopalan, G., and M. Yoo, 2026: A statistician’s overview of physics-informed neural networks for spatio-temporal data. Journal of the American Statistical Association. link to paper

Zhang, L., Ma, X., Wikle, C.K., and R. Huser, 2026: Fast and flexible emulation of spatial extremes processes via variational autoencoders. Journal of the American Statistical Association. link to paper

North, J.S., Wikle, C.K., and E.M. Schliep, 2025: A Bayesian approach for spatio-temporal data-driven dynamic equation discovery. Bayesian Analysis. 20(2): 375-404. link to paper

Grieshop, N. and C.K. Wikle, 2024: Echo state network-enhanced symbolic regression for spatio-temporal binary stochastic cellular automata. Spatial Statistics, 60. link to paper

Yoo, M., and C.K. Wikle, 2024: A Bayesian spatio-temporal level set dynamical model and application to fire front propagation. Annals of Applied Statistics. 18, 404-423. link to paper

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