Noah Paulson
Computational Scientist
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolNoah Paulson is a computational scientist at Argonne National Laboratory with over a decade of experience applying data-driven and Bayesian methods to materials science and thermodynamics. He has led multidisciplinary teams and secured significant funding (lead PI for $740K and co-PI on $2M) to develop open-source tools such as a Phase Diagram Uncertainty Quantification Python package and predictive workflows for battery lifetime and Ti microstructure performance. His work blends rigorous PhD-level modeling with practical software development, delivering orders-of-magnitude speedups in predictive workflows and reduced-order models for additive manufacturing. Based in Chicago, he bridges experimental and computational efforts, having designed combined experimental-computational studies and scalable Python pipelines. Notably, he couples uncertainty quantification and extrapolative functional-form choices to make thermodynamic predictions robust across extreme temperatures.
12 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Mechanical Engineering, 4.00/4.00, Doctor of Philosophy (Ph.D.), Mechanical Engineering, 4.00/4.00 at Georgia Institute of TechnologyB.S., Mechanical Engineering, B.S., Mechanical Engineering at Tufts UniversityUniversity of Auckland
English
Github Skills (27)
python9
monte-carlo9
bayesian8
machine-learning8
algorithms8
data-science8
simulation7
thermodynamics7
doi7
uncertainty-quantification7
scientific-computing7
bayesian-inference6
algorithm6
visualization6
scipy6
Programming languages (2)
Github contributions (5)
tonyfast/nsf-goali
Aug 2014 - Jan 2015
Contributions:189 commits, 55 pushes in 5 months
npaulson/UnaryBayes
Jun 2018 - May 2021
A Bayesian framework for the selection, calibration and uncertainty quantification of thermodynamic property models
Contributions:8 commits, 1 PR, 6 pushes in 2 years 11 months
bayesianuncertainty-quantification
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