John Pearson
Associate Professor Of Neurobiology
Durham, North Carolina, United States
Summary
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Rockstar🎓
Top SchoolJohn Pearson is an associate professor of neurobiology at Duke University with 13 years of experience applying machine learning to neural and behavioral data. He studies how organisms learn complex motor skills without external reinforcement and how simple computational principles shape early sensory systems, while developing real-time analysis tools for neural experiments. Trained as a physicist (PhD Princeton), he blends theoretical rigor with hands-on coding—contributing to open-source projects like JuliaStats/Distributions.jl to improve statistical tooling for multivariate and mixture models. His work uniquely bridges experimental design, algorithm development, and live-data intervention, enabling experiments that adapt as they run. Based in Durham, NC, he leads Pearson Lab and mentors interdisciplinary teams at the intersection of computation and neurobiology.
13 years of coding experience
15 years of employment as a software developer
BS, Physics, Mathematics, BS, Physics, Mathematics at University of KentuckyPhD, Physics, PhD, Physics at Princeton University
Github Skills (6)
statistics10
probability-distribution10
distributions10
julia10
testing10
data-science9
Programming languages (10)
Github contributions (5)
JuliaStats/Distributions.jl
Jan 2016 - Aug 2016
A Julia package for probability distributions and associated functions.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:55 commits, 22 PRs, 22 pushes in 6 months
Contributions summary:John's contributions primarily focused on enhancing the `distributions.jl` package, which involves probability distributions and associated functions. Their work included fixing type restrictions within functions like `params` for various distributions (geometric, multivariate, matrix vars, and mixtures) to avoid fallback and improve functionality. The user also added and tested parameter retrieval methods to many distributions within the package and its tests, specifically with respect to multivariate distributions, further ensuring the robustness of the library and testing the contract of these distributions. This suggests a focus on extending the functionality and ensuring the accuracy of the package's statistical capabilities.
julia-packagedistributionsstatisticsdata-scienceprobability-distributions
jmxpearson/rlfit
Oct 2013 - Jun 2019
Simple Matlab code to fit reinforcement learning models to choice data
Contributions:21 commits, 1 PR, 5 pushes in 5 years 8 months
matlabreinforcement-learningfitmatlab-codereinforcement
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