Nathan Woods
Visualization Engineer
Idaho Falls, Idaho, United States
Summary
👤
Senior🎓
Top SchoolNathan Woods is a Visualization Engineer and physicist with 13 years of experience applying advanced mathematics and modern programming to scientific prediction, analysis, and visualization. He has built production scientific software in Matlab, Python, Fortran, and C across academia, national labs, and government research, and currently works at Idaho National Laboratory after a decade at Los Alamos. Nathan’s work spans verification of multiphysics simulation codes, development of integrative Method of Manufactured Solutions, and CFD tools that simplify grid generation while resolving shocks and moving boundaries. He is an active open-source contributor to SciPy, notably implementing and improving nquad to enhance multi-variable numerical integration. Trained as an aerospace PhD and experienced in micro- and nanofabrication at NASA internships, he blends rigorous theory with hands-on engineering and mentoring. Colleagues rely on him for mathematically robust solutions to complex simulation and visualization challenges.
13 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Aerospace Engineering Sciences, Doctor of Philosophy (Ph.D.), Aerospace Engineering Sciences at University of Colorado BoulderB.S., Physics, B.S., Physics at Brigham Young University
English, Spanish
Github Skills (9)
algorithms10
scipy10
python10
scientific-computing10
numpy10
documentation8
c115
fortran5
c175
Programming languages (6)
Github contributions (5)
scipy/scipy
May 2013 - Jan 2017
SciPy library main repository
Role in this project:
Back-end Developer
Back-end DeveloperContributions:14 commits, 5 PRs, 42 comments in 3 years 8 months
Contributions summary:Nathan significantly contributed to the `scipy/scipy` repository, focusing on numerical integration functionalities. Their primary contribution involved the implementation of the `nquad` function, designed for integrating over multiple variables. Further commits demonstrate improvements to the `nquad` documentation and overall codebase, and also converting existing dblquad and tplquad functions to use nquad. Their work added new features and improved the usability of the SciPy library for scientific computing.
scipypythonscientific-computing
woodscn/BACL-MMS
Jun 2013 - Nov 2014
Contributions:78 commits in 1 year 5 months
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