#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

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Oct 2, 2025

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.

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ADVICE

Scale Bayes With Pragmatic Strategies

Reduce dataset size with aggregation before full Bayesian inference when data are massive.
Use variational inference or GPU-accelerated frameworks like NumPyro when aggregation isn't feasible.

ANECDOTE

Rust Reimplementation Origin Story

Gabriel reimplemented PyMC BART's particle Gibbs sampler in Rust to speed up inference and integrate with Python.
He started by translating Python code into Rust and then optimized hotspots rather than rewriting from scratch.

INSIGHT

BART Is Boosting With Bayesian Uncertainty

BART combines many trees like boosting but provides a posterior over functions, giving principled uncertainty estimates.
That uncertainty makes BART suitable as a surrogate in decision and optimization workflows where confidence matters.

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Takeaways:

BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.

Chapters:

05:10 – From economics to IoT and Bayesian statistics

18:55 – Introduction to BART (Bayesian Additive Regression Trees)

24:40 – Re-implementing BART in Rust for speed and scalability

32:05 – Comparing BART with Gaussian Processes and other tree methods

39:50 – Strengths and limitations of BART

47:15 – Handling missing data and different likelihoods

54:30 – Variational inference and big data challenges

01:01:10 – Embedding BART into optimization and decision-making frameworks

01:08:45 – Open source, PyMC, and community support

01:15:20 – Advice for newcomers

01:20:55 – Future of BART, Rust, and probabilistic programming

Thank you to my Patrons for making this episode possible!

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