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@patrick-kidger
patrick-kidger
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Patrick Kidger patrick-kidger

Building a JAX ecosystem: Equinox, Diffrax, Lineax, etc. ---- SciML, numerics, neural ODEs ---- Solo traveller, martial artist, scuba diver!

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patrick-kidger/README.md

I've written alot of numerical JAX and PyTorch, now used in diverse applications across science (simulation of black holes, soil moisture, ...) and ML (large language models, large protein models, ...). I would particularly highlight:

  1. Equinox: elegant neural networks.GitHub Repo stars

  2. Diffrax: numerical ODE/SDE solvers.GitHub Repo stars

  3. jaxtyping: shape/dtype annotations for arrays. (Also supports PyTorch etc, despite the name!)GitHub Repo stars

A full list of other libraries

JAX

  1. Lineax: linear/least-squares solvers.GitHub Repo stars

  2. Optimistix: root finding, least squares, etc.GitHub Repo stars

  3. sympy2jax: optimise your symbolic expressions via gradient descent!GitHub Repo stars

  4. Quax: multiple dispatch in JAX!GitHub Repo stars

  5. ESM2quinox: ESM2 implemented in JAX.GitHub Repo starsnew!

Python

  1. Wadler-Lindig: A better Python pretty-printer, based upon the theory of Wadler and Lindig.GitHub Repo stars

Publishing

  1. MkPosters: Write academic posters in Markdown, style them with CSS, save them to PDF. No wrestling with LaTeX.GitHub Repo stars

  2. typst_pyimage: A Typst extension adding support for generating figures using inline Python code.GitHub Repo stars

  3. mkdocs_ipynb: Use*.ipynb files (Jupyter notebooks) when building documentation with MkDocs.

  4. mkdocs_include_exclude_files: Include or exclude specific files when building documentation with MkDocs.

  5. action_update_python_project: GitHub CI/CD to automatically deploy Python projects to PyPI and GitHub when a version is bumped.

Me:

I am currently a tech lead on ML for protein engineering (lead optimization) atCradle Bio, and founded much of the open-source scientific JAX ecosystem. I also hold an honorary lectureship at Imperial College London. I previously worked at Google X, and received my PhD from Oxford on neural differential equations.

My current interests include pretty much anything related to scientific machine learning and scientific computing! I've now worked across diverse parts of the field, from modern deep learning (protein language models) to classical methods (numerics), to everything in between (neural differential equations).

I am also known for having strong opinions on the importance of good software development! :)

Other links:

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  1. equinoxequinoxPublic

    Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

    Python 2.3k 155

  2. google-research/torchsdegoogle-research/torchsdePublic

    Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

    Python 1.6k 205

  3. diffraxdiffraxPublic

    Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

    Python 1.6k 144

  4. NeuralCDENeuralCDEPublic

    Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

    Python 645 70

  5. jaxtypingjaxtypingPublic

    Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/

    Python 1.3k 70

  6. optimistixoptimistixPublic

    Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

    Python 383 19

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