A fresh approach to Technical Computing

Julia is a fast and flexible language for technical computing. While young among programming languages, at just over five years since its public release, it's rapidly gaining momentum. The community of around 150,000 users (and growing) have together built over 1300 packages (and counting). Scientists and engineers in particular are excited about Julia's ability to move ideas rapidly from prototype to production, with a flexible and interactive workflow that doesn't compromise on performance. You can learn more about Julia’s features at http://julialang.org/.

Compared to more established languages, Julia's package library is small and still coming of age – but for you, that's a great opportunity! Joining us for Google Summer of Code is a great way not only to build the packages and features you've always wanted and learn about something new, but also for those contributions to become core parts of Julia's ecosystem and used by many thousands of people. The scope of possible work is endless, but could include areas as diverse as developing the web stack, improving data munging facilities, hacking the compiler, or building packages for areas as life sciences, finance, physics, data science, machine learning, or something completely different! If that sounds interesting to you, please send an application, or reach out to us at https://discourse.julialang.org/ with any questions.

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Technologies

  • julia
  • javascript
  • gpu
  • c/c++
  • llvm

Topics

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The Julia Language 2017 Projects

  • Yiannis Simillides
    A Wrapper for the FEniCS Finite Element Toolbox
    We will create a wrapper for the FEniCS toolbox in Julia, using PyCall.jl , to allow Julia users direct access to the FEniCS functionality. This will...
  • Akshay Sharma
    An Artificial Neural Network Differential Equation solver for Julia
    The purpose of this project is to develop a flexible library of NN solvers (NeuralNetDiffEq.jl) which are able to utilize parallelization, and plugs...
  • Shivin Srivastava
    Efficient Finite Difference Discretizations of Partial Differential Operators
    JuliaDiffEq is the most popular github organization for solving differential equations in Julia.Currently, it has an assorted collection of Ordinary...
  • Dorisz Albrecht
    Hamiltonian Approximate Bayesian Computation
    Likelihood-based methods have many appealing features, but they are difficult to use for models which do not have a closed-form, tractable likelihood...
  • Animesh Kashyap
    Image Segmentation
    Image Segmentation is described as one of the most important aspects of image processing. Image segmentation is the process of partitioning an image...
  • Tejus Gupta
    Image Segmentation and HOG features
    I propose to Add a package for image segmentation as a part of JuliaImages with the following algorithms * Thresholding - Otsu’s method and...
  • Jameson Quinn
    Improving and demonstrating Julia statistical models' MCMC: CrossCat example
    General-purpose statistical modeling tools such as OpenBugs and Stan allow using easy-to-build modular likelihood models to fit models to data...
  • Sebastian Pfitzner
    Integration of Documentation facilities into the Juno IDE
    Juno as an IDE (and, since the introduction of JuliaPro, the quasi-standard IDE for Julia) has matured quite a bit during the last years, but the...
  • Sarvjeet Ghotra
    Middlewares for common web application chores
    Implementation of mid-level features - specifically routing, load-balancing, cookie/session handling, and authentication.
  • Harmen Stoppels
    Native Julia implementations of iterative solvers for numerical linear algebra
    The IterativeSolvers.jl package is currently lacking some well-known iterative methods such as QMR and BiCGStab(l) for solving linear systems and...
  • Yingbo Ma
    Native Julia Solvers for Ordinary Differential Equations Boundary Value Problem: A GSoC proposal
    This is a proposal for the project "Native Julia solvers for ordinary differential equations and algebraic differential equations". There is no...
  • Zhang Shiwei
    New Features for Flux.jl
    Flux.jl is a high-level neural network library in Julia, which has an intuitive API, easy to debug, and performant. One of the key features of...
  • KrishnaKanhaiya
    Optimization and Bayesian Techniques for the parameter estimation of Differential Equations
    Differential equation models are widely used in many scientific fields that include engineering, physics and biomedical sciences. The so-called...
  • Divyansh
    Parallel Graph development
    Parallel Graph development I am interested in working on parallel graph algorithms and an efficient data structure to represent Graphs, which take...
  • Kenta Sato (bicycle1885)
    Parallelism in Bio.jl
    Support parallelism in Bio.jl based on Dagger.jl
  • Joel Mason
    Rebuilding Interact.jl with WebIO.jl
    Interactive data exploration is an increasingly important component of data science. Interact.jl and Escher.jl are good libraries that have the...
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2017