General Lab Information

Nathan Urban

Group Leader / Computational Scientist, Applied Mathematics, Computational Science Initiative

Nathan Urban

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-8807
nurban@bnl.gov

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 | Education | Appointments | Publications | Awards


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

Education

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

Professional Appointments

  • 2020-present, Group Leader and Computational Scientist, Applied Mathematics, Brookhaven National Laboratory
  • 2021-present, Adjunct Assistant Professor, Institute for Advanced Computational Science, Stony Brook University
  • 2020-present, Guest Scientist, Statistical Sciences (CCS-6), Los Alamos National Laboratory
  • 2012-2020, Scientist, Computational Physics and Methods (CCS-2), Los Alamos National Laboratory
  • 2011-2012, Scientist, Institutes Office (Energy Security Center), Los Alamos National Laboratory
  • 2010-2011, Science, Technology, and Environmental Policy (STEP) Fellow, Princeton School of Public and International Affairs, Princeton University
  • 2007-2010, Research Associate / Postdoctoral Scholar, Geosciences, Penn State
  • 1999-2006, Graduate Assistant, Physics, Penn State
  • 1997-1999, Software Developer, InSystems Technologies

Selected Publications

  • Tang L, Dutta S, Klein N, et al (2024) Julia for HPC: In Situ Data Analysis with Julia for Climate Simulations at Large Scale. JuliaCon Proceedings 6:134. https://doi.org/10.21105/jcon.00134
  • Jantre S, Urban NM, Qian X, Yoon B-J (2024) Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp48485.2024.10448265
  • Yu S, Hannah W, Peng L, et al (2023) ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
  • Rumsey K, Grosskopf MJ, Lawrence E, et al (2022) A hierarchical sparse Gaussian process for in situ inference in expensive physics simulations. Applications of Machine Learning 2022. https://doi.org/10.1117/12.2633427
  • Yoon J, Romero-Lankao P, Yang YCE, et al (2022) A Typology for Characterizing Human Action in MultiSector Dynamics Models. Earth's Future. https://doi.org/10.1029/2021ef002641
  • Banesh D, Panda N, Biswas A, et al (2021) Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. 2021 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata52589.2021.9671929
  • Dutta S, Klein N, Tang L, Wolfe JD, Roekel LV, Benedict JJ, Biswas A, Lawrence E, Urban N (2021) In Situ Climate Modeling for Analyzing Extreme Weather Events. ISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization. doi: 10.1145/3490138.3490142
  • Grosskopf M, Lawrence E, Biswas A, Tang L, Rumsey K, Van Roekel L, Urban N (2021) In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes. ISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization. doi: 10.1145/3490138.3490140
  • Berdahl M, Leguy G, Lipscomb WH, Urban NM (2021) Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties. The Cryosphere 15:2683–2699. doi: 10.5194/tc-15-2683-2021
  • Melland P, Albright J, Urban NM (2021) Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme. Machine Learning: Science and Technology 2:025015. doi: 10.1088/2632-2153/abd644
  • Foster D, Comeau D, Urban NM (2020) A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability. Journal of Climate 33:6065–6081. doi: 10.1175/jcli-d-19-0769.1
  • DeGennaro AM, Urban NM, Nadiga BT, Haut T (2019) MODEL STRUCTURAL INFERENCE USING LOCAL DYNAMIC OPERATORS. International Journal for Uncertainty Quantification 9:59–83. doi: 10.1615/int.j.uncertaintyquantification.2019025828
  • DeGennaro AM, Urban NM (2019) Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation. SIAM Journal on Scientific Computing 41:A1482–A1499. doi: 10.1137/17m115414x
  • Jonko A, Urban NM, Nadiga B (2018) Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data. Climatic Change 149:247–260. doi: 10.1007/s10584-018-2232-0
  • Urrego-Blanco JR, Urban NM, Hunke EC, et al (2016) Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model. Journal of Geophysical Research: Oceans 121:2709–2732. https://doi.org/10.1002/2015jc011558
  • Urban NM, Holden PB, Edwards NR, Sriver RL, Keller K (2014) Historical and future learning about climate sensitivity. Geophysical Research Letters 41:2543–2552. doi: 10.1002/2014gl059484
  • Little CM, Oppenheimer M, Urban NM (2013) Upper bounds on twenty-first-century Antarctic ice loss assessed using a probabilistic framework. Nature Climate Change 3:654–659. doi: 10.1038/nclimate1845
  • Fricker TE, Oakley JE, Urban NM (2012) Multivariate Gaussian Process Emulators With Nonseparable Covariance Structures. Technometrics 55:47–56. doi: 10.1080/00401706.2012.715835
  • Schmittner A, Urban NM, Shakun JD, Mahowald NM, Clark PU, Bartlein PJ, Mix AC, Rosell-Melé A (2011) Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum. Science 334:1385–1388. doi: 10.1126/science.1203513
  • Cui Y, Kump LR, Ridgwell AJ, Charles AJ, Junium CK, Diefendorf AF, Freeman KH, Urban NM, Harding IC (2011) Slow release of fossil carbon during the Palaeocene–Eocene Thermal Maximum. Nature Geoscience 4:481–485. doi: 10.1038/ngeo1179
  • Urban NM, Fricker TE (2010) A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model. Computers & Geosciences 36:746–755. doi: 10.1016/j.cageo.2009.11.004
  • Isenberg NM, Mertins SD, Yoon B-J, et al (2024) Identifying Bayesian optimal experiments for uncertain biochemical pathway models. Scientific Reports 14:. https://doi.org/10.1038/s41598-024-65196-w

Awards & Recognition

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

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-8807
nurban@bnl.gov

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