Nathan Urban

Nathan Urban serves as Group Leader and Computational Scientist for the Applied Mathematics group at Brookhaven National Laboratory's Computational Science Initiative (CDS), a position held since 2020. Concurrently, he holds adjunct and guest appointments at Stony Brook University's Institute for Advanced Computational Science and Los Alamos National Laboratory's Statistical Sciences group. His interdisciplinary career bridges computational mathematics, statistics, and environmental science with emphasis on decision support under uncertainty.

His educational foundation includes multiple degrees from Virginia Tech (1997): B.S. in Physics, Computer Science, and Mathematics, followed by advanced degrees from Penn State University (2006): Ph.D. and M.Ed. in Physics.

• Ph.D. in Physics, Penn State University (2006)
• M.Ed. in Physics, Penn State University (2006)
• B.S. in Physics, Virginia Tech (1997)
• B.S. in Computer Science, Virginia Tech (1997)
• B.S. in Mathematics, Virginia Tech (1997)

Urban's research centers on uncertainty quantification and Bayesian inference methodologies applied to complex scientific systems. His work develops frameworks for probabilistic prediction, multi-model uncertainty analysis, reduced order modeling, and scientific machine learning. Key application domains include climate science, ice sheet dynamics, and molecular design, where he addresses challenges in decision making under uncertainty and optimal experimental design. His expertise spans computational statistics, hybrid physical-data driven modeling, and in-situ data analysis for large-scale simulations.

Recent publications (2021-2024) reveal a pronounced shift toward integrating machine learning with physical modeling, particularly in climate and environmental systems. His work demonstrates innovative applications of active subspaces, Gaussian processes, and deep generative models to quantify epistemic uncertainties in ice sheet projections, molecular design, and extreme weather analysis. A consistent theme is developing scalable methods for uncertainty quantification in high-dimensional systems.

Urban received significant recognition through the DOE Office of Science Early Career Research award (2013) in Biological and Environmental Research, supporting his foundational work in climate model uncertainty.

• Early Career Research award, DOE Office of Science (Biological and Environmental Research), 2013

As Applied Mathematics group leader at Brookhaven, Urban directs research in computational statistics and mathematical modeling for scientific applications. His team develops advanced methods for uncertainty quantification and decision support across multiple DOE mission areas, with strong collaborations in climate science and materials research.