Sage Hahn is a Senior Software Engineer based in New York with eight years of experience bridging academic research and production ML systems. Holding a PhD in Complex Systems and Data Science from the University of Vermont, Sage has applied advanced time-series, network analysis, and neuroimaging methods to large-scale studies and shipped domain-specific tooling and visualization libraries. At DeepHealth they moved from software engineer to senior engineer, translating research-grade algorithms into clinical-focused software. Comfortable across the ML stack, Sage combines rigorous statistical modelling with practical engineering practices and a track record of contributing to open-source research software. Unusually for an industry engineer, they maintain an active publication record and a portfolio site showcasing reproducible projects that reflect both theoretical depth and production sensibility.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Complex systems and Data Science, Doctor of Philosophy - PhD, Complex systems and Data Science at University of Vermont
The Brain Predictability toolbox (BPt), is a python based Machine Learning library designed primarily for tabular and neuroimaging specific neuroimaging data but can easily be generalized further.
Contributions:18 releases, 1446 commits, 94 PRs in 3 years 6 months
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Sage Hahn - Senior Software Engineer at DeepHealth