Erhardt & Hepler receive NSF Grant

Rob Erhardt and Staci Hepler’s grant proposal “Scalable Models, Fast Computation and Predictability for Spatio-temporal Ordinal Data” was awarded by the National Science Foundation. The project will develop new statistical methodology for ordinal spatio-temporal data, and deploy it to improve drought prediction in conjunction with the National Drought Mitigation Center. Rob Erhardt and Staci Hepler will develop the Bayesian methodology and computational algorithm for fitting and updating the model. They will be joined by Courtney Di Vittorio (WFU Engineering) and Lauren Lowman (WFU Engineering), who will build the data pipeline and study water resource management implications of enhanced drought prediction. The project runs from 2022-2025 and is funded by NSF DMS.