Nathan Urban is the group leader of the Applied Mathematics group at Brookhaven National Laboratory's Computational Science Initiative (CSI). He holds a Ph.D. in condensed matter physics from Penn State, and has previously held research positions at Los Alamos National Laboratory, Princeton, and Penn State. His research interests include Bayesian inference and spatiotemporal statistics, probabilistic prediction and forecasting, multi-model / model-form / model structural uncertainty quantification, reduced order modeling, scientific machine learning and hybrid physical-data driven modeling, in-situ/streaming data analysis at scale, information fusion, decision making under uncertainty and optimal experimental design, and integrated multiscale computational frameworks for decision support.

Expertise

• Uncertainty quantification, Bayesian inference, and computational statistics
• Surrogate and reduced order modeling
• System identification
• Scientific machine learning
• Optimization
• Climate science, impacts, vulnerability, and adaptation