Jaron Thompson is a modeling-focused scientist and engineer with eight years of research and applied experience bridging chemical engineering, statistical modeling, and experimental lab work. He holds a PhD in Chemical Engineering from the University of Wisconsin–Madison and has applied stochastic and statistical approaches to problems ranging from soil microbiome carbon sequestration to biophysical tissue models. Jaron’s background includes internships and research at Los Alamos National Laboratory and industry R&D roles where he combined programming, image analysis, CAD, and hands-on wet-lab techniques. Now a modeler at Ark in Greater Boston, he brings a rare blend of advanced quantitative modeling, experimental assay design, and practical device prototyping to interdisciplinary teams. Colleagues value his ability to translate complex biological systems into testable models and reproducible lab workflows.
8 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Chemical Engineering, Doctor of Philosophy - PhD, Chemical Engineering at University of Wisconsin-Madison
Master's degree, Chemical Engineering, Master's degree, Chemical Engineering at Colorado State University
Python implementation of methods described in "Pattern Recognition and Machine Learning" by Christopher Bishop to perform Bayesian Linear regression on synthetic data.
Contributions:2 PRs, 23 pushes, 3 branches in 5 years 7 months
Contributions:8 commits, 4 pushes in 1 year 6 months
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