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Achievement: Pair Extraordinairex4Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: YOLOAchievement: Arctic Code Vault Contributor

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michaelellis003/README.md

Hi, I'm Michael!

I'm a data scientist with a math and statistics background who likes to build things. My work spans Bayesian methods and causal inference to LLMs, GenAI, and AI agents. I believe good data science requires good software engineering. Currently, I'm especially interested in the intersection of causal inference and agentic AI, building systems that use causal reasoning for counterfactual simulation and intervention planning.

Featured Work

  • smcjax — Sequential Monte Carlo and particle filtering in JAX (docs)
  • lmxlab — Language model research platform for Apple Silicon on MLX (docs)
  • pypkgkit — CLI tool for scaffolding production-ready Python packages (PyPI)
  • gaussian-process — Bayesian spatial inference with Gaussian processes in R

Open Source Contributions

Tech Stack

Python JAX NumPyro MLX PyTorch R C++ TypeScript SQL Docker uv

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  1. lmxlab lmxlab Public

    Language model experimentation on Apple Silicon using MLX

    Python

  2. smcjax smcjax Public

    Sequential Monte Carlo and particle filtering in JAX

    Jupyter Notebook

  3. variational-inference variational-inference Public

    Variational Bayes and CAVI algorithms from Ormerod & Wand — approximate Bayesian inference examples in R

    R 2

  4. gaussian-process gaussian-process Public

    Full Bayesian inference for Gaussian process spatial models using MCMC in R

    R 1

  5. sequential-monte-carlo-hmm sequential-monte-carlo-hmm Public

    Master's thesis: Sequential Monte Carlo methods for inference in Hidden Markov Models

  6. pypkgkit pypkgkit Public template

    Production-ready Python package template with uv, ruff, pyright, and GitHub Actions CI/CD

    Python