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JAX scientific ML ecosystem:

Probably the reason you're here. I would 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. GitHub Repo stars

  4. Lineax: linear/least-squares solvers. GitHub Repo stars (new!)

  5. Optimistix: root finding, least squares, etc. GitHub Repo stars (new!)

Other links:

Me:

I currently wear multiple hats across bio/ML/CS at Cradle.bio. These days I am generally interested in scientific ML, and specifically the application of ML to unsolved problems in biology!

I also hold an honorary lectureship at Imperial College London. In past lives I previously wore the same multitude of hats at Google X, and did my PhD at the University of Oxford.

Patrick Kidger's Projects

torchcde icon torchcde

Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

torchcubicspline icon torchcubicspline

Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, and backpropagation.

torchdiffeq icon torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

torchsde icon torchsde

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

torchtyping icon torchtyping

Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

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