Garrett Jenkinson
Sr Principal Knowledge Graph Engineer at Johnson & Johnson
Rochester, Minnesota, United States
Summary
👤
Senior🎓
Top SchoolGarrett Jenkinson is a Sr. Principal Knowledge Graph Engineer with a decade of interdisciplinary experience bridging AI, knowledge graphs, bioinformatics, and statistical modeling. He has moved between academia and industry—from deep computational epigenetics and network modeling at Johns Hopkins and Mayo Clinic to leading data science and knowledge-graph initiatives at Xilis and now Johnson & Johnson—demonstrating rare fluency in both molecular biology and production ML systems. Garrett designs and operationalizes knowledge-driven AI that connects complex biological data (single-cell, methylation, imaging) to product and clinical workflows, pairing rigorous mathematical modeling with pragmatic project leadership. His background in electrical engineering and applied mathematics underpins a quantitative approach to NLP, image processing, and graph-based inference, and he often translates esoteric research insights into deployable analytics. Based in Rochester, MN, he brings an academic rigor and systems-thinking lens to enterprise-scale knowledge engineering.
10 years of coding experience
6 years of employment as a software developer
Johns Hopkins UniversityMS Electrical and Computer Engineering, MS Electrical and Computer Engineering at Carnegie Mellon University
Github Skills (49)
conditionals10
science10
julia10
programming-language9
machine-learning9
hpc9
bioinformatics9
ordinal9
genomics9
rna-seq8
keras8
petsc8
root-finding8
pipeline8
numerical8
Programming languages (6)
Github contributions (5)
GarrettJenkinson/InformMe.jl
Nov 2019 - Aug 2022
Contributions:1 release, 58 commits, 37 pushes in 2 years 8 months
GarrettJenkinson/informME
Feb 2017 - Jun 2018
An information-theoretic pipeline for methylation analysis of WGBS data
Contributions:113 commits, 7 PRs, 87 pushes in 1 year 4 months
wgbspipelinegenomicsbioinformaticsmethylation
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