Tom Holoien
Research Scientist at The Aerospace Corporation
Irvine, California, United States
Summary
👤
Senior🎓
Top SchoolTom Holoien is a Research Scientist with a decade of experience applying software engineering and statistical modeling to telescope and remote sensing data, currently at The Aerospace Corporation. He builds C++ and Python tools for instrument control and large-scale image pipelines, and specializes in signal detection, image subtraction, machine learning, and MCMC-driven physical model fitting. Previously an observational astrophysicist, he helped design the ASAS-SN survey's automated processing and detection systems and has a track record of extracting rare, high-energy signals from noisy ground- and space-based data. Tom combines hands-on engineering with prolific scientific output—16 first-author papers as a trainee and contributions to over 150 peer-reviewed articles—and has mentored students who published extensively. Based in Irvine, CA, he pairs rigorous statistical methods with practical systems engineering to turn large, messy datasets into robust physical insight.
9 years of coding experience
Bachelor of Science - BS, Astrophysics, Bachelor of Science - BS, Astrophysics at Rutgers University–New BrunswickDoctor of Philosophy - PhD, Astronomy, Doctor of Philosophy - PhD, Astronomy at The Ohio State UniversityBachelor of Arts - BA, East Asian Studies, Bachelor of Arts - BA, East Asian Studies at Stanford University
Github Skills (10)
pls10
lime10
density-estimation10
resampling10
galaxy8
python8
scikit7
scikit-learn7
machine-learning7
datasets5
Programming languages (1)
Github contributions (3)
tholoien/empiriciSN
Jun 2016 - Nov 2022
Contributions:1 release, 53 commits, 5 PRs in 6 years 5 months
parametershostcorrelationsgalaxyrealistic
tholoien/XDGMM
Aug 2016 - Nov 2022
A wrapper class for the scikit-learn BaseEstimator class that implements both the astroML and Bovy et al. (2011) XDGMM methods.
Contributions:2 releases, 100 commits, 28 PRs in 6 years 3 months
limepythonplsmachine-learningdensity-estimation
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