Virgile Andreani is a software engineer in Paris with 12 years of experience who blends a physicist’s rigor and a PhD in computational biology into production-quality scientific and probabilistic software. He is a core contributor to the widely used PyMC project, focusing on back-end improvements to variational inference, distributions, and sampling—work that keeps a complex Bayesian ecosystem current with evolving Python internals. Passionate about Rust, Monte Carlo methods, and high-performance scientific computing, he brings deep numerical intuition to software design and maintenance. Colleagues rely on him to translate advanced statistical ideas into robust, maintainable code that survives deprecation cycles and API changes. An unassuming polymath, he combines academic research habits with pragmatic engineering to move models from prototype to reliable tooling.
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Back-end Developer
Contributions:52 reviews, 43 commits, 42 PRs in 4 months
Contributions summary:Virgile's commits primarily focus on fixing deprecation warnings and adjusting code related to updating the PyMC library. Specifically, the user modified code for variational inference, distributions, and sampling functionalities. These changes involved updates to internal functions and data structures to address deprecated features and incorporate shape-related updates within the context of a Bayesian modeling and probabilistic programming framework.
Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.
Contributions:2 PRs, 1 branch in 6 years 2 months
golangalphago-zerozeroalphagoknowledge
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