Brandon Benton
Researcher IV Data And Software Engineer
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolBrandon Benton is a Researcher IV and data/software engineer with a PhD in computational physics and a decade of experience applying machine learning, high-performance computing, and data engineering to climate and renewable energy problems. At NREL he maintains the National Solar Radiation Database and develops GAN-based super-resolution pipelines for wind forecasting, previously porting large climate models to AWS and running petaflop-scale simulations at Cornell. His work blends deep domain expertise in fluid dynamics, aerodynamics, and climate variability with practical production engineering—shipping reproducible pipelines, novel albedo models, and automated reprocessing systems. Brandon also has a track record of turning messy, disparate datasets into usable databases (e.g., tree-ring chronologies) and building tools that make complex simulations accessible to broader audiences. Based in Seattle, he pairs strong publication and grant experience with hands-on side projects—from quadcopters to moderation bots—highlighting a curiosity for real-world systems and scalable research software.
10 years of coding experience
12 years of employment as a software developer
BS - University Honors and Magna cum Laude, Physics, BS - University Honors and Magna cum Laude, Physics at Georgia Southern UniversityDoctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Cornell University
Github Skills (32)
machine-learning10
generative-adversarial-network10
sky10
super-resolution10
renewable-energy10
climate-change10
deep-learning10
tensorflow10
solar-energy10
sarif9
abstract-syntax-tree9
energy9
geospatial9
perl9
extraction9
Programming languages (5)
Github contributions (5)
NatLabRockies/nsrdb
Jan 2024 - Jun 2026
NSRDB data processing pipeline. Includes satellite data assimilation, cloud property prediction and gap-filling, radiative transport modeling, and data collection.
Contributions:9 releases, 36 PRs, 250 pushes in 2 years 5 months
machine-learningnrelsolar-energy
NatLabRockies/sup3r
Jan 2022 - Jan 2023
The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
Contributions:10 releases, 421 reviews, 1006 commits in 11 months
generative-adversarial-networksuper-resolutionmachine-learningrenewable-energydeep-learning
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