John Zito is an Assistant Research Professor and fifth-year PhD candidate in statistics with 10 years of research and applied experience spanning Bayesian methods and time series analysis. He combines academic teaching and mentorship—having led REU projects, capstone teams forecasting ER volumes, and multiple statistics courses—with hands-on research at institutions like the Federal Reserve Bank of Cleveland where he coauthored peer-reviewed Bayesian macroeconometrics work. An active open-source contributor, he has extended JuliaStats/Distributions.jl with matrix-variate distributions and robust unit tests, demonstrating both theoretical depth and practical software engineering. Based in Durham, NC, he blends rigorous probabilistic modeling with reproducible software practice, often translating complex methods into interpretable tools for students and policymakers.
10 years of coding experience
PhD, Statistics, PhD, Statistics at Rice University
BA, Mathematics, BA, Mathematics at Kenyon College
A Julia package for probability distributions and associated functions.
Role in this project:
Back-end Developer
Contributions:13 reviews, 37 commits, 34 PRs in 1 year 3 months
Contributions summary:John primarily contributed to the `distributions.jl` Julia package by implementing and testing new probability distributions and related functions. They added the `MatrixNormal`, `MatrixTDist`, `MatrixBeta`, and `MatrixFDist` distributions, including constructors, properties, and sampling methods. Furthermore, the user fixed and tested conversion methods for existing distributions and added new unit tests.
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John Zito - Assistant Research Professor at Duke University