Gabriel Stechschulte
Bayesian software developer and contributor to PyMCBART who re-implemented BART sampling in Rust to improve performance and interoperability with PyMC; knowledgeable about particle Gibbs, tree-based models, and probabilistic programming.
Best podcasts with Gabriel Stechschulte
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Oct 2, 2025 • 1h 10min
#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
Gabriel Stechschulte is a software engineer specializing in Bayesian methods and optimization. He discusses the power of Bayesian Additive Regression Trees (BART) for uncertainty quantification and its re-implementation in Rust, enhancing performance for big data. Gabriel explores how BART contrasts with other models, its strengths in avoiding overfitting, and its integration into optimization frameworks for decision-making. He also emphasizes the importance of open-source communities, encouraging newcomers to contribute actively.
Oct 9, 2025 • 23min
BITESIZE | How Bayesian Additive Regression Trees Work in Practice
Gabriel Stechschulte, a Bayesian software developer known for his work with PyMCBART, dives into the re-implementation of Bayesian Additive Regression Trees (BART) in Rust. He discusses the technical hurdles and enhanced performance achieved through this project. Gabriel explains the value of BART in uncertainty quantification and how it contrasts with other tree-based methods. The conversation also covers practical aspects, like integrating BART with Python and balancing open-source contributions with a full-time job, all while exploring the innovative features of PyMCBART.
