Christopher Fonnesbeck is a principal data scientist and academic biostatistician with 11+ years applying Bayesian and hierarchical modeling to medicine, epidemiology, and sports analytics. He bridges rigorous research and production code—contributing substantive sampling and model-conditioning fixes to the core PyMC project and modernizing PyMC examples and tooling—while leading data science at PyMC Labs and teaching at Vanderbilt. His background spans quantitative ecology and statistics (PhD/MS) and a decade of applied work with MLB organizations, where decision analysis and sabermetrics informed real-world choices. Known for pragmatic statistical software improvements, he often surfaces subtle numerical fixes in probabilistic samplers that improve inference stability.
11 years of coding experience
17 years of employment as a software developer
University of Georgia
M.S. Zoology, M.S. Zoology at The University of British Columbia
THIS IS THE **OLD** PYMC PROJECT (VERSION 2). PLEASE USE PYMC INSTEAD:
Role in this project:
Back-end Developer
Contributions:3 releases, 23 commits, 79 PRs in 3 years 5 months
Contributions summary:Christopher primarily contributed to bug fixes and enhancements within the PyMC2 project. Their work includes adding Python 3 compatibility to existing code, fixing a divide-by-zero error, and addressing equation references in documentation. Furthermore, the user was involved in incrementing the version number and removing deprecated arguments. These changes suggest a focus on maintaining and updating the codebase.
Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics
Role in this project:
Data Scientist
Contributions:315 commits, 7 PRs, 435 pushes in 5 years 8 months
Contributions summary:Christopher overhauled a Bayesian computation notebook, introducing concepts related to Bayesian inference and estimation. They implemented example models using a binomial model and beta distributions to demonstrate posterior estimation through optimization. The user also worked on model comparison by fitting various models and used criteria to select the best model.
centerpythonr-programminglinear-modelsstatistical
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