Matthew Earl
Nuclear Engineer at Innovative Scientific Solutions, Inc
Ogden, Utah, United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Earl is a nuclear engineer and master's researcher at the Air Force Institute of Technology with 14 years of technical experience spanning CFD, thermal modeling for additive manufacturing, and full-stack software development. With a BSME focused on fluid mechanics and hands-on IT support experience, he blends deep computational and mathematical skills with practical systems know-how—he taught himself to code at 13 and has pursued advanced math independently. His recent research derived computational/analytic thermal models for Laser Powder Bed Fusion and he has applied ML techniques to projects like an automatic number-plate recognition system. Now at Innovative Scientific Solutions, he brings interdisciplinary rigor to nuclear engineering problems while maintaining an active coding portfolio that surfaces creative, data-driven solutions.
14 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Mechanical Engineering, Bachelor of Science - BS, Mechanical Engineering at Cedarville UniversityMaster of Science - MS, Nuclear Engineering, Master of Science - MS, Nuclear Engineering at U.S. Air Force Institute of Technology
Github Skills (7)
neural-network10
computer-vision10
machine-learning10
tensorflow10
python10
image-processing10
image-generation9
Programming languages (4)
Github contributions (5)
matthewearl/deep-anpr
Mar 2016 - Aug 2016
Using neural networks to build an automatic number plate recognition system
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 1 PR, 10 pushes in 5 months
Contributions summary:Matthew primarily focused on developing and refining a number plate recognition system using neural networks. Their contributions include experimenting with model parameters, updating the image generation code to incorporate variability, and modifying the training process to detect the presence of number plates. The user also increased the model size and simplified the output layer, indicating an iterative approach to improving model performance. These changes suggest active involvement in model development and optimization.
neural-network
matthewearl/height-map-vis
Jan 2015 - Feb 2015
Contributions:57 commits, 25 pushes, 1 branch in 27 days
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.