Summary
George Willingham is a Senior Machine Learning Engineer and physicist with eight years of experience solving R&D challenges in sensing, signal processing, and data science. He builds physics-informed algorithms and optimized ML models for electromagnetic remote sensing, cognitive radio, and sensor characterization, moving solutions from inverse-problem research to rapid prototype and validation. His background in condensed matter experiments and topological phonon modeling gives him a rare mix of hands-on UHV measurement experience and theoretical/numerical rigor. At Leidos he applied Bayesian and sparse methods to inverse problems and developed ML-driven signal-processing pipelines; he now brings that expertise to systems-level ML work at Mirion. Based in Virginia, he blends instrument-level insight with production-minded model engineering, often discovering simpler physics-based priors that improve model robustness. Colleagues rely on him for pragmatic approaches that bridge experimental nuance and scalable algorithm design.
8 years of coding experience
7 years of employment as a software developer
Bachelor of Science, Physics, Bachelor of Science, Physics at Tulane University
Master's degree, Physics, Master's degree, Physics at Boston University
English, German