PeopleofSuperintelligence

Researcher

Nathan Urban

Also known as Nathan M. Urban

Nathan Urban is an applied mathematician whose work on uncertainty quantification and scientific machine learning supports climate science. After earning bachelor's degrees at Virginia Tech in 1997 and a physics Ph.D. at Penn State in 2006, he moved into climate uncertainty at Penn State, Princeton and Los Alamos National Laboratory, and received a DOE Early Career Research award in 2013. He joined Brookhaven National Laboratory in 2020 and coauthored ClimSim, a hybrid physics-ML climate emulation dataset that won an Outstanding Datasets and Benchmarks Paper award at NeurIPS 2023. Since 2025, he is group leader of Brookhaven's Optimal Experimental Design & Uncertainty Quantification group.

Now at
Applied Mathematics Department, Brookhaven National Laboratory
Badges

Timeline

  1. 1997–2006

    Virginia Tech and Penn State degrees

    After studying at Virginia Tech, where he earns bachelor's degrees in physics, computer science and mathematics in 1997, he works as a software developer at InSystems Technologies until 1999. He is a physics graduate assistant at Penn State from 1999 and completes a Ph.D. and M.Ed. in physics (computational statistical mechanics) there in 2006.

  2. 2007–2011

    Postdocs at Penn State and Princeton

    From 2007 to 2010, he is a research associate and postdoctoral scholar in geosciences at Penn State; from 2010 to 2011, he is a Science, Technology, and Environmental Policy fellow at Princeton's School of Public and International Affairs. In 2011, he is a coauthor of a Science paper led by Andreas Schmittner of Oregon State University that estimates climate sensitivity from Last Glacial Maximum temperature reconstructions.

  3. 2011–2020

    Scientist at Los Alamos

    He joins Los Alamos National Laboratory in 2011 as a scientist in the Institutes Office (Energy Security Center). From 2012 to 2020, he is a scientist in Computational Physics and Methods (CCS-2), and in 2020 he becomes a guest scientist in Statistical Sciences (CCS-6).

  4. 2013

    A DOE Early Career award

    He receives a 2013 Early Career Research award from the DOE Office of Science (Biological and Environmental Research) for work on multi-model climate uncertainties. His Nature Climate Change paper with Christopher Little and Michael Oppenheimer, published in 2013, applies a Bayesian probabilistic framework to upper bounds on twenty-first-century Antarctic ice loss.

  5. 2020–2025

    Applied Mathematics group at Brookhaven

    In 2020, he joins Brookhaven National Laboratory as group leader and computational scientist in Applied Mathematics, within the Computational Science Initiative. His research interests include Bayesian inference and spatiotemporal statistics, reduced order modeling and scientific machine learning. In 2021, he also becomes an adjunct assistant professor at Stony Brook University's Institute for Advanced Computational Science.

  6. 2023

    ClimSim wins at NeurIPS

    He is among the authors of ClimSim, a dataset of multi-scale climate simulations with 5.7 billion pairs of input and output vectors, built for hybrid physics-ML research. The paper receives an Outstanding Datasets and Benchmarks Paper award in the dataset category at NeurIPS 2023.

  7. 2024

    Active subspaces for Bayesian deep learning

    With Sanket Jantre, Xiaoning Qian and Byung-Jun Yoon, he publishes a paper at ICASSP 2024 that builds a low-dimensional active subspace of a neural network's parameters. The reduced subspace makes Bayesian inference by Monte Carlo sampling or variational inference scalable.

  8. Since 2025

    Leading an uncertainty quantification group

    In 2025, he becomes group leader and computational scientist of the Optimal Experimental Design & Uncertainty Quantification group in Brookhaven's Applied Mathematics Department, part of the Computing & Data Sciences directorate. His research interests include scientific machine learning, hybrid physical-data driven modeling and decision making under uncertainty.

Career

  1. Since 2025Applied Mathematics Department, Brookhaven National LaboratoryGroup Leader and Computational Scientist
  2. From 2021Stony Brook UniversityAdjunct Assistant Professor
  3. 2020–2025Brookhaven Science AssociatesGroup Leader and Computational Scientist
  4. 2011–2020Triad National SecurityScientist, Guest Scientist
  5. 2010–2011Princeton UniversityScience, Technology, and Environmental Policy Fellow
  6. Until 1999InSystems TechnologiesSoftware Developer
  7. 1999–2010Pennsylvania State UniversityPhysics Graduate Assistant, Research Associate and Postdoctoral Scholar in geosciences

Education

  1. 2006Pennsylvania State UniversityPh.D. and M.Ed. in physics
  2. 1997Virginia TechBachelor's degrees in physics, computer science, and mathematics

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Nathan Urban. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorateIncludes Princeton University. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorate. Across 2,116 people with both an undergraduate degree and a doctorate recorded, we trace which undergraduate schools feed which doctoral programs, how often students stay at the same school, and how paths differ by birth-country region.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Princeton University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  4. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Princeton University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Nathan Urban yet. Send us a free-licensed photo.

  1. BNL | Staff | Nathan Urban, Computational Science bnl.gov. Checked 21 October 2026.
  2. Projecting the Future: Quantifying Uncertainties in How the Climate Will Change - Computational Science Research Center csrc.sdsu.edu. Checked 21 October 2026.
  3. Climate sensitivity to carbon dioxide more limited than extreme projections, research shows | ScienceDaily sciencedaily.com. Checked 21 October 2026.
  4. Upper bounds on twenty-first-century Antarctic ice loss assessed using a probabilistic framework | Nature Climate Change nature.com. Checked 21 October 2026.
  5. Nathan Urban | Institute for Advanced Computational Science iacs.stonybrook.edu. Checked 21 October 2026.
  6. Announcing the NeurIPS 2023 Paper Awards – NeurIPS Blog blog.neurips.cc. Checked 21 October 2026.
  7. NeurIPS 2023 Awards neurips.cc. Checked 21 October 2026.
  8. Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks arxiv.org. Checked 21 October 2026.