Yoo et al. (2025+). Stochastic Integro-Differential Models for Animal Trajectories, submitted.
Yoo, M. and Wikle, C. K. (2024). A Bayesian Spatio-temporal Level Set Dynamic Model and Application to Fire Front Propagation, Annals of Applied Statistics, 18, 404-423, https://doi.org/10.1214/23-AOAS1794
Yoo, M. and Wikle, C. K. (2023). Using echo state networks to inform physical models for fire front propagation, Spatial Statistics, 54, 100732, https://doi.org/10.1016/j.spasta.2023.100732
Yoo, M., Zhang, L., Wikle, C.K., Opitz, T. (2025+). Modeling high and low extremes with a novel dynamic spatio-temporal model, submitted. arXiv preprint: https://arxiv.org/abs/2508.01481
Wikle, C.K., North, J., Gopalan, G., Yoo, M. (2026). A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data, Journal of the American Statistical Association, 1-17, https://doi.org/10.1080/01621459.2026.2625420
Yoo et al. (2025). Emulation with uncertainty quantification of regional sea‐level change caused by the Antarctic Ice Sheet. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000349, https://doi.org/10.1029/2024JH000349
Yoo, M., Barreto, D.W., Hooten, M.B. (2026+). Making recursive Bayesian inference robust, submitted. arXiv preprint: https://arxiv.org/abs/2606.0798
Yoo, M. and Kim, D. (2019). Statistical tests for biosimilarity based on relative distance between follow-on biologics for ordinal endpoints, Communications for Statistical Applications and Methods, 27, 1-14, https://doi.org/10.29220/CSAM.2020.27.1.001
Wikle, C.K., Zhang, L., Yoo, M. (2026+). Deep Bayesian spatio-temporal modeling of wildfires, Bayesian Deep Learning, CRC/Routledge, submitted.
Yoo. M., Zhang, L., Wikle, C.K. (2026). Real-time forecasting of fire front propagation using the level set method and echo state networks. Environmental Modelling with Contemporary Statistics, 265-284, Chapman and Hall/CRC.