Garrett Barter
Group Manager at National Laboratory of the Rockies
Denver, Colorado, United States
Summary
👤
Senior🎓
Top SchoolGarrett Barter is a systems and computational engineer with 8+ years of experience leading technical teams and translating complex analyses into policy and investment decisions, now managing a group at the National Laboratory of the Rockies in Denver. He built his technical foundation at MIT with an SB, SM, and PhD in Aerospace Engineering and has applied that rigor across national labs and aerospace firms including Sandia and Ball Aerospace. Garrett combines hands-on optimization and QA work—contributing algorithmic fixes to the well-known OpenMDAO project—with domain expertise in wind and renewable energy systems. He is comfortable navigating both research-grade modeling and production-facing engineering, whether improving optimization drivers or steering multidisciplinary teams. Colleagues describe him as pragmatic and detail-oriented, with a soft spot for renewable energy that shapes his strategic priorities.
8 years of coding experience
7 years of employment as a software developer
PhD Aerospace Engineering, PhD Aerospace Engineering at Massachusetts Institute of Technology
Github Skills (11)
algorithm10
unit-testing10
optimisation10
python10
optimizers10
optimization10
application-framework9
app-framework9
web-framework9
genetic-algorithm8
numpy8
Programming languages (12)
Github contributions (5)
OpenMDAO/OpenMDAO
Apr 2022 - Apr 2022
OpenMDAO repository.
Role in this project:
Back-end Developer / QA Engineer
Back-end Developer / QA EngineerContributions:7 commits, 1 PR, 6 comments in 7 days
Contributions summary:Garrett primarily worked on the OpenMDAO drivers, focusing on algorithmic optimizations and testing. They made changes to the `differential_evolution_driver.py` and `genetic_algorithm_driver.py` files, aligning the initialization of populations for different drivers. Further contributions include fixing and altering unit tests to reflect the code changes and ensure the integrity of the optimization algorithms. The user's work involved iterative adjustments and reversion of changes, indicating a focus on debugging and resolving discrepancies within the framework.
openmdaooptimization-methodsnonlinear-optimizationgurobifenics
conda-forge/moorpy-feedstock
Aug 2023 - Dec 2024
A conda-smithy repository for moorpy.
Contributions:1 review, 4 PRs, 3 pushes in 1 year 4 months
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