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Previous editions: 2017 | 2016 | 2015 | 2014
Sacha Verweij and Jane Herriman

Stanford University and Julia Computing

An Introduction to Julia

Are you new to Julia?! This beginners’ tutorial should be accessible to anyone with technical computing needs and some experience with another language. We will show you why Julia is special, demonstrate how easy Julia is to learn, and get you writing some Julia code.

Andy Ferris

Fugro Roames

A practical introduction to metaprogramming in Julia

Julia focuses on speed and user productivity, due in part to its metaprogramming capability. This workshop arms you with the knowledge to create fast, generic and easy-to-use APIs using techniques including multiple dispatch, recursion, traits, constant propagation, macros, and generated functions.

Pontus Stenetorp

University College London

Machine Learning with Julia: Elegance, Speed and Ease

Machine Learning has become one of the hottest research and industry areas over the last few years; we believe Julia is the strongest contender to become the language for Machine Learning and in this tutorial we will give a flying start to train/deploy models and use of the power that Julia brings.

Sheehan Olver

Imperial College, London

Numerical Analysis in Julia

This workshop brings together 5 speakers on different topics in numerical analysis, to demonstrate the strengths of Julia’s approach to scientific computing in atomistic simulations, function approximation, differential equations, fast transformations, validated numerics, and linear algebra.

David Anthoff

University of California, Berkeley


This workshop will introduce the Queryverse family of packages, a unified data science stack on julia. It provides tools for file IO, data querying, visual data exploration and statistical plotting. It also integrates with a large number of other julia packages.

Chris Rackauckas

UC Irvine and MIT

Solving Partial Differential Equations with Julia 

Climate scientists solve fluid dynamics PDEs. Biologists solve reaction-diffusion PDEs. Economists solve optimal control PDEs. But solving PDEs is hard! Where do you start? This workshop gives a broad overview of the Julia package ecosystem and shows how to tie it together to solve these problems.

Juan Pablo Vielma

MIT Sloan

The JuMP ecosystem for mathematical optimization 

JuMP is an award-winning DSL for mathematical optimization that has quickly become the gold-standard for its simplicity, performance, and versatility. A major overhaul of JuMP will be finalized during the JuMP-dev workshop in June, so it is the perfect time for an updated tutorial and feature demo.

Avik Sengupta

Julia Computing

Natural Language Processing in Julia

A hands on workshop demonstrating the use of natural language processing tools in Julia. Working with textual data, we will discuss methods for data collection, parsing, pre-processing, embedding, classification and deep neural networks.