Project Grant 2433548

This $185,163 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of scalable Gaussian process methods for spatial statistics and machine learning. The project aims to address computational challenges in applying Gaussian processes to large datasets by developing novel algorithms and software that can enable accurate spatial prediction, calibration, parameter learning, and nonparametric regression at scale. The research will result in a nearly universal toolbox for scalable Gaussian process modeling with broad applicability across scientific and engineering domains, including carbon monitoring, renewable energy, rainfall prediction, robotic calibration, and insurgency modeling. The award was made to the University of Wisconsin System, which will implement the project through its University of Wisconsin - Madison division.