My primary work Is focused on the development and application of methods for spatio-temporal data, with a particular focus on dynamics. These are best summarized In the two books, Statistics for Spatio-Temporal Data (with Noel Cressie), and Spatio-Temporal Statistics with R (with Andrew Zammit-Mangion and Noel Cressie), which are given In the "Books" page on this website. Recent methodological work Is primarily related to the following areas (see my CV for a much more complete listing):

Hybrid Neural-Statistical Models for Spatio-Temporal Data

Wikle, C.K. and A. Zammit-Mangion, 2022: Deep learning for spatial and spatio-temporal data. (In Review)

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.

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.

Zammit-Mangion, A. and Wikle, C.K., 2020. Deep integro-difference equation models for spatio-temporal forecasting. Spatial Statistics, 30.

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.

McDermott, P.L. and C.K. Wikle, 2019: Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatio-temporal data. Entropy, 21(2), 184; https://doi.org/10.3390/e21020184.

McDermott, P.L. and C.K. Wikle, 2019: Deep echo state networks with uncertainty quantification for spatio-temporal forecasting. Environmetrics, 30, https://doi.org/10.1002/env.2553.

McDermott, P.L., and C.K. Wikle, 2017: An ensemble quadratic echo state network for nonlinear spatio- temporal forecasting. Stat, 6, 315–330, doi:10.1002/sta4.160.

Emulating Complex Spatio-Temporal Data

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.

Bayesian Methodology and Computation

Raim, A.R., Holan, S.H., Bradley, J.R., and C.K. Wikle, 2021: An R package for spatio-temporal change of support, Computational Statistics, 36, 749–780, https://doi.org/10.1007/s00180-020-01029-4.

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.

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.

Bradley, J.R., Wikle, C.K., and S.H. Holan, 2019: Spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution, Journal of Time Series Analysis, 40: 363382.

Katzfuss, M., Stroud, J.R., and C.K. Wikle, 2019: Extended ensemble Kalman filters for high-dimensional hierarchical state-space models. Journal of the American Statistical Association, 6, 1–43.

Bradley, J.R., Holan, S.H., and C.K. Wikle, 2018. Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data. Bayesian Analysis (Invited Discussion Article), 1: 253–281.

Stroud, J.R., Katzfuss, M., and C.K. Wikle, 2018: A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation. Monthly Weather Review, 146: 373–386.

Bradley, J.R., Wikle, C.K., and Holan, S.H., 2016: Regionalization of multiscale spatial processes using a criterion for spatial aggregation error. Journal of the Royal Statistical Society, Series B, doi:10.1111/rssb.12179.

Bradley, J.R., Wikle, C.K., and Holan, S.H., 2016: Bayesian spatial change of support for count-valued survey data. Journal of the American Statistical Association, 111, 472–487.

Visualization of Spatial and Spatio-Temporal Data

Lucchesi, L.R., Kuhnert, P.M., and C.K. Wikle, 2021: Vizumap: an R package for visualising uncertainty in spatial data. Journal of Open Source Software, 6(59), 2409. https://doi.org/10.21105/joss.02409.

Lucchesi, L.R., and C.K. Wikle, 2017: Visualizing uncertainty in areal data estimates with bivariate choropleth maps, map pixelation, and glyph rotation. STAT, 6, 292–302, doi:10.1002/sta4.150.