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@ChrisRackauckas

Christopher Rackauckas

ChrisRackauckas
Cambridge, MA

I am interested in the development of high performance numerical software for scientific computing and scientific machine learning. JuliaDiffEq is my main project, which includes DifferentialEquations.jl and its ordinary, stochastic, delay, algebraic, and discrete stochastic, and partial differential equation solvers, along with their parallelism and parameter analysis tooling. Included in this domain is the DiffEqFlux.jl framework for neural differential equations, and the outlying ecosystem of differentiable programming for the Julia programming language. In addition, the Pumas.jl project for precision personalized medicine via pharmaceutical modeling and simulation is included in this sphere of scientific software.

However, my work spans throughout Julia, acting as a maintainer for core libraries such as Plots.jl to writing the core documentation for the Juno IDE, has lead JuliaObserver.com to show that I have had the most (or second most) commits to Julia packages, with additional projects including (but not limited to) ParallelDataTransfer.jl, DataStructures.jl, MATLAB.jl, etc. all with the common goal of building a platform for next-generation scientific computing.

Current sponsors 1

@nrontsis

Past sponsors 3

@Leticia-maria
@mo8it
@taskswithcode

Featured work

  1. SciML/DifferentialEquations.jl

    Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…

    Julia 2,784
  2. SciML/DiffEqFlux.jl

    Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

    Julia 844
  3. SciML/diffeqpy

    Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

    Python 500
  4. SciML/diffeqr

    Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

  5. UCIDataScienceInitiative/IntroToJulia

    A Deep Introduction to Julia for Data Science and Scientific Computing

    HTML 249

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Saying thanks. This is a "Netflix-level" sponsorship to show appreciation for the work!

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Platinum Tier. You are a saint. At this level, I will make a strong attempt to ensure that your issues are responded to in a timely manner.

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At this level, I will try to ensure some level of support for your project, including adding the right snippets of code and MWEs into the standard test suites of the JuliaDiffEq packages and beyond to ensure that your usage is stable, and inform you of any upcoming changes, with pull-requests for known breaking changes.

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Let me know what specific features you need (within reason of course) and I'll move them up the priority list.

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Wow, all I can say is thanks and let me know what you need for you to get your job done and I'll try to make sure you get weekly updates.