Bryan Brzycki is a PhD-trained astrophysicist and machine-learning researcher with 11 years of experience building quantitative models and signal-detection pipelines for radio astronomy and SETI. He applies convolutional neural networks and object-detection–inspired techniques to noisy spectrograms, and authored the open-source Python package setigen to generate realistic synthetic radio signals for ML training. A Harvard alum with a PhD from UC Berkeley, he has a track record of packaging research code into reusable libraries (e.g., magnolia) and running large-scale simulations on HPC systems. Bryan blends rigorous statistical analysis with practical software engineering, shipping end-to-end pipelines that go from data generation to classification. Based in New York, he often bridges academic publication goals with reproducible tooling and maintainable code for long-term scientific use.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of California, Berkeley
Bachelor’s Degree, Astrophysics, Bachelor’s Degree, Astrophysics at Harvard University
High School, Astrophysics, High School, Astrophysics at Troy High School
Contributions:2 PRs, 12 pushes, 4 branches in 2 years 6 months
analysis-packageanalysispythonblossom
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