Chris Fonnesbeck
Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government, including 7 years in pro baseball research with the Philadelphia Phillies, New York Yankees, and Milwaukee Brewers.
He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.
Sessions
Most machine learning methods give you a prediction but not a measure of how much to trust it. Bayesian Additive Regression Trees (BART) combine the flexibility of tree ensembles (e.g. random forests, boosting) with full uncertainty quantification—every prediction comes with a probability interval, not just a point estimate. This hands-on tutorial introduces BART through three applications: regression, classification, and survival analysis. Using pymc-bart, participants will learn to fit flexible models that automatically capture non-linear relationships while providing honest uncertainty estimates. We emphasize practical interpretation throughout: visualizing predictions with uncertainty bands, understanding variable importance, and interpreting model output.
Come build something with the PyMC development team.
Code sprints are collaborative working sessions where contributors of all experience levels tackle meaningful open issues side by side. Whether you want to squash a long-standing bug, sharpen the documentation, build a worked example, or simply understand how a major open-source project operates from the inside — there's a place for you here.
PyMC is the most widely used probabilistic programming library in Python, and the people who build it will be in the room. Bring your laptop; we'll handle the rest.