Nate Dzbenski
Research Scientist at Decision Sciences
Greensboro--Winston-Salem--High Point Area United States
Summary
👤
Senior🎓
Top SchoolNate Dzbenski is a research scientist and machine-learning engineer with eight years of experience applying physics-first approaches to real-world sensing and data problems. He leads algorithmic redesigns for cosmic-ray muon detectors—revamping track fitting, momentum estimation, and image reconstruction using C++, Python, Kalman filters, SART/uCT/MLEM, and modern ML toolkits like PyTorch and TensorRT. Nate pairs hands-on simulation work in Geant4 and Garfield++ with cloud-first ML pipeline development across GCP/Azure/AWS, bringing academic detector techniques into production-grade systems. His background includes PhD-level experimental high-energy physics, Monte Carlo and finite-element modeling, and practical electronics for particle detectors, plus experience building ethical, DevOps-driven predictive models for university systems. Uncommonly, his work eliminated costly lookup tables by simulating electron drift dynamics, demonstrating a blend of deep physics insight and pragmatic engineering.
9 years of coding experience
8 years of employment as a software developer
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at University of North Carolina at WilmingtonDoctor of Philosophy - PhD, Experimental High-Energy Physics, Doctor of Philosophy - PhD, Experimental High-Energy Physics at Old Dominion University
Github Skills (6)
detector10
particle-physics9
geant49
simulation-framework9
simulation9
ruby7
Programming languages (3)
Github contributions (5)
ndzbenski/rgf_analysis
Feb 2020 - Mar 2020
Contributions:206 pushes, 1 branch in 10 days
ndzbenski/rgf
Feb 2020 - Oct 2020
Contributions:137 pushes, 1 branch in 8 months
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