Joshua Loyal
Data Scientist
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJoshua Loyal is a data scientist and PhD-trained statistician who applies experimental particle physics rigor to predictive modeling and large-scale data problems. With 11 years of experience, he has analyzed ~100 TB datasets at institutions including CERN and Argonne and currently develops production analytics at DataRobot while teaching statistics at UIUC. He builds end-to-end tools in Python and C++ (ROOT, TMVA, scikit-learn) and has led projects that improved signal classification using deep learning and ensemble methods. His work blends scientific publication-grade inference—one paper cited by multiple later studies—with practical engineering: recovering corrupted data, running Linux batch analyses, and maintaining collaborative codebases. Known for clear technical communication across international teams, he pairs strong statistical intuition with hands-on systems and database experience to turn noisy, high-dimensional measurements into actionable models.
11 years of coding experience
1 year of employment as a software developer
Bachelor of Science (B.S.), Physics and Mathematics, 3.82/4.00, Bachelor of Science (B.S.), Physics and Mathematics, 3.82/4.00 at Duke UniversityMaster's Degree, Physics, Master's Degree, Physics at Yale UniversityUniversity of Illinois Urbana-Champaign
Latin
Github Skills (36)
pytorch10
dimensionality-reduction10
python10
data-science10
feature-selection10
statistics10
machine-learning10
dimension-reduction10
deep-learning10
tensorflow10
variable-selection10
scikit-learn10
jax10
data-analysis10
network-analysis9
Programming languages (3)
Github contributions (5)
joshloyal/drforest
Feb 2021 - Jan 2022
Dimension Reduction Forests
Contributions:1 release, 159 commits, 6 PRs in 10 months
dimension-reductiondata-sciencemachine-learningrandom-foreststatistics
joshloyal/multidynet
Mar 2021 - Mar 2023
Contributions:1 release, 59 pushes in 2 years
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