Patrick Mccreery is a Ph.D. candidate and research scientist combining astrophysics and applied math with eight years of research and software experience, primarily using Python to analyze stellar and exoplanet atmospheres for JWST programs. At Johns Hopkins and the Space Telescope Science Institute he has derived ultra-precise stellar parameters and elemental abundances to probe giant planet formation and led uniform analyses of atmospheric escape and limb-darkening improvements for JWST tools. He graduated summa cum laude in Astrophysics with an honors thesis on generating synthetic solar granulation using machine learning, and has applied ML to solar acoustic oscillations and flare simulations. Comfortable in both research and teaching roles, he also writes for the Applied Mathematics program and brings a rare mix of observational, theoretical, and software-focused expertise to exoplanetary science.
8 years of coding experience
4 years of employment as a software developer
University of Colorado Denver
Johns Hopkins University
Bachelor of Arts - BA, Astrophysics, Bachelor of Arts - BA, Astrophysics at University of Colorado Boulder
Contributions:79 PRs, 65 pushes, 18 branches in 5 months
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