Jeffrey Adrion
Staff Population Geneticist, ML Science
Salt Lake City, Utah, United States
Summary
👤
Senior🎓
Top SchoolJeffrey Adrion is a Staff Population Geneticist at Ancestry with a decade of experience applying machine learning and deep learning to large-scale genomic problems. He builds production-ready, scalable algorithms that blend deep learning, computational biology, and software engineering while prioritizing interpretability, reproducibility, and long-term infrastructure. His work spans academia and industry—from developing robust neural-network methods and standardized simulation models during a postdoc to leading ML-driven genomic products at Ancestry. Notably, he has a strong publication record and an NSF Graduate Research Fellowship, reflecting both rigorous research and practical delivery. Based in Salt Lake City, he thrives on turning complex population-genetic ideas into deployable systems that advance large-scale genomic inference.
9 years of coding experience
10 years of employment as a software developer
Bachelor of Science - BS, Zoology, summa cum laude, Bachelor of Science - BS, Zoology, summa cum laude at North Carolina State UniversityDoctor of Philosophy - PhD, Population Genetics, Doctor of Philosophy - PhD, Population Genetics at Indiana University Bloomington
Github Skills (17)
recurrent10
recurrent-neural-networks10
genetic10
simulate9
genetics9
genomics9
illumina9
sequence8
landscape8
bioinformatics8
simulation8
deep-learning7
population-genetics6
python6
machine-learning5
Programming languages (3)
Github contributions (5)
jradrion/TEFLoN
Jul 2017 - Mar 2021
TEFLoN uses paired-end illumina sequence data to discover and genotype transposable elements present in your samples.
Contributions:29 commits, 2 PRs, 88 pushes in 3 years 7 months
discoversequenceelementsilluminagenotype
kr-colab/ReLERNN
May 2019 - Mar 2021
Recombination Landscape Estimation using Recurrent Neural Networks
Contributions:1 release, 97 commits, 29 PRs in 1 year 9 months
deep-learningrecurrent-neural-networksrecombinationneural-networksestimation
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