Freddy Obrecht is a research data scientist with 11 years of experience applying statistical analysis, machine learning, and large-scale scientific computing to complex problems in physics and reverse software engineering. Holding a PhD in particle physics, he has built high-performance C++ analysis chains, Monte Carlo simulations, and clustering algorithms to extract weak signals from TB-scale experimental data. At Johns Hopkins APL he leads efforts on graph neural networks, translating decompiled C into symbolic math, and advancing deep learning on homomorphically encrypted data—demonstrating a rare blend of applied cryptography and program-understanding research. He pairs practical skills in Linux cluster automation, OCR, and data visualization with a knack for turning messy, multi-source datasets into actionable models. Colleagues rely on him for principled, reproducible analysis and for turning domain-specific research challenges into production-capable algorithms.
11 years of coding experience
5 years of employment as a software developer
Master's degree, Physics, Master's degree, Physics at University of Connecticut
Bachelor of Arts - BA (Double major), Physics, Geophysics and Planetary Science, Bachelor of Arts - BA (Double major), Physics, Geophysics and Planetary Science at Boston University
Contributions:13 pushes, 1 branch in 3 years 8 months
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