Steven Berguin
Principle Software Engineer at Archer
Atlanta, Georgia, United States
Summary
👤
Senior🎓
Top SchoolSteven Berguin is a Principal Software Engineer and operations research specialist with eight years of applied experience bridging optimization, simulation, and software development for defense and aerospace customers. Based in Atlanta, he has led mixed-integer and multidisciplinary optimization efforts—from coordinating airborne laser intercepts to aircraft maintenance scheduling—turning complex math into actionable decision-support tools using Python, automatic differentiation, and interactive Jupyter interfaces. His work repeatedly narrowed multi-year roadblocks into months-long successes and seeded sponsored transitions, including a $150K AFRL contract and production-ready capabilities. Steven combines deep aerospace research (PhD, Georgia Tech) with hands-on systems engineering at Raytheon and GTRI, and he’s practiced devops for legacy scientific code to achieve order-of-magnitude runtime improvements. Notably, he has a pattern of reducing problem dimensionality and building visual, AI-augmented tools that make physics-based tradeoffs accessible to non-experts.
8 years of coding experience
10 years of employment as a software developer
Bachelor's Degree Aerospace Engineering, Bachelor's Degree Aerospace Engineering at The University of Texas at AustinDoctor of Philosophy (Ph.D.) Aerospace Engineering, Doctor of Philosophy (Ph.D.) Aerospace Engineering at Georgia Institute of TechnologyGraduate Certificate Applied Trajectory Optimization, Graduate Certificate Applied Trajectory Optimization at Naval Postgraduate School
French, English
Github Skills (22)
sampling10
gradient10
derivative10
predictive-modeling10
toolbox9
conda9
mixture-of-experts9
least-squares9
density-estimation9
modeling9
surrogate9
machine-learning8
conda-forge8
neural-network5
openmdao4
Programming languages (4)
Github contributions (5)
shb84/JENN
Apr 2018 - Jul 2020
Jacobian-Enhanced Neural Networks (JENN) are fully connected multi-layer perceptrons, whose training process was modified to account for gradient information. Specifically, the parameters are learned by minimizing the Least Squares Estimator (LSE), modified to minimize prediction error of both response values and partial derivatives.
Contributions:1 release, 19 commits, 34 PRs in 2 years 3 months
least-squaresneural-networkgenngradientjacobian
shb84/smt
Jan 2019 - Dec 2025
Surrogate Modeling Toolbox
Contributions:2 PRs, 26 pushes, 2 branches in 6 years 11 months
toolboxmodelingsurrogate
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