Federal Grant Award Summary

Drexel University received a $100,000 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program, effective June 1, 2026 through May 31, 2029. This collaborative research project will develop statistical theory and methodology for quantifying rates of change and gradients in spatiotemporal datasets, with application to identifying boundaries that track significant changes across space and time. The project will produce scalable Bayesian statistical methods and software tools designed to analyze large, complex spatiotemporally-indexed data prevalent in biomedical and neuroimaging research.

The research deliverables include low-rank projection-based approximations to Gaussian processes, scalable Bayesian factor models, and graphical predictive processes for multivariate spatiotemporal data analysis. The project will conduct rigorous statistical investigations into rates of change within predictive frameworks and develop probability distributions to facilitate posterior inference using Bayesian methods. Additionally, the award supports graduate student research training and will extend statistical inference techniques to smooth surfaces in space-time that track rapid directional changes. These methodological advances will strengthen the nation's capacity for data-driven discovery across multiple scientific and engineering domains where assessing regions of rapid change is critical.