This workshop is geared towards anyone who wants to start using Julia.
It will be an extremely accessible overview of Julia.

Green

Jose Storopoli

Introduction to Julia

Graph computing is an innovative technology that allows developers to build applications and systems as directed acyclic graphs (DAGs). Graph computing offers generic solutions to some of the most fundamental challenges in enterprise computing such as scalability, transparency and lineage. In this workshop, we survey the available graph computing tools in Julia, then walk through a few hands-on examples of building real world applications and systems using graph computing.

Yadong Li

Introduction to Graph Computing

A three-hour introductory workshop for newcomers to Julia and machine
learning. Participants will have training in some technical domain,
for example, in science, economics or engineering. While no prior
experience with Julia or machine learning is needed, it is assumed 
participants have Julia 1.7 installed on their computer.

Anthony Blaom

Samuel

Getting started with Julia and Machine Learning

Medical image segmentation with Julia 
Participants can download data before task 9 from https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2 
Additionally tou can download required libraries to your enviroment
]add ProgressMeter StaticArrays BSON Distributed Flux Hyperopt Plots MedEye3d Distributions Clustering IrrationalConstants ParallelStencil CUDA HDF5 MedEval3D Colors

Jakub Mitura

GPU accelerated medical image segmentation framework

Statistics is a domain where some early stage development of packages, and some early applications, have come about in Julia. We think of this mini-symposium as a combination of (a) Report on many interesting recent developments in this field and (b) Offer a birds eye view to the people interested in this field, and help them assess the state of maturity so as to make decisions about whether Julia is appropriate for their statistics work.

Ajay Shah

Statistics symposium

The JuliaMolSim community is hosting a minisymposium! Come hear about AtomsBase, our project to create a unified interface for representing atomic geometries, as well as packages for simulation (both quantum mechanical and classical particles-based) and machine learning on atomistic systems. Do you have an idea for a package you think the community needs? Participate in our “quick pitch” session and find co-developers to help build it!

Rachel Kurchin

JuliaMolSim: Computation with Atoms

Come get your feet wet with Oceananigans.jl, a native Julia, fast, friendly, flexible and fun ocean model. In the first half of this workshop participants will be helped to run and analyze one of several simple ocean problems. The problems relate to state-of-the-art challenges in climate science and computational science. In the second half we will examine Julia language features, packages and design choices that enable Oceananigans.jl.

Chris Hill

Francis Poulin

Gregory Wagner

Valentin Churavy

Simone Silvestri

Tomas Chor

Suyash Bire

Rodrigo Duran

Jean-Michel Campin

Hands-on ocean modeling and ML with Oceananigans.jl

This session has moved to Zoom. Please join with Zoom ID: 6376486897 at 2PM Israel time. 

This (Hebrew language) workshop provides an introduction to the Julia language for machine learning engineers, data-scientists, and statisticians. Attendees will gain a solid entry point for using Julia as their preferred data analysis tool.

Yoni Nazarathy

Introduction to Julia with a focus on statistics (in Hebrew)

Makie.jl is a Julia-native interactive data visualization library. In this workshop, participants will learn how to create complex interactive and static plots, using the full range of tools Makie has to offer. Topics could include writing custom recipes, understanding the scene graph, mastering the layout system, handling complex observable structures and tweaking visual styles. The workshop will also be an opportunity to learn about the architecture and underlying ideas of Makie.

Simon Danisch

Julius Krumbiegel

Interactive data visualizations with Makie.jl

A fundamental Julia experience is feeling the power of computing at your fingertips - but how many of us squeeze the absolute most out of the Julia REPL? In this workshop, you'll go from 0 to hero in about 3 hours with a whopping collection of all the tips, tricks and goodies you could ever hope for in your Julia REPL experience.

Miguel Raz Guzmán Macedo

Julia REPL Mastery Workshop

This minisymposium will feature the use of the differentiable programming paradigm applied to Earth System Models (ESMs). The goal is to exploit derivative information and seamlessly combine PDE-constrained optimization and scientific machine learning (SciML). Speakers will address (1) Why differentiable programming for ESMs; (2) What ESM applications are we targeting?; and (3) How are we realizing differentiable ESMs? Target ESMs include ice sheet, ocean, and solid Earth models.

Patrick Heimbach

Nora Loose

Mathieu Morlighem

Boris Kaus

Sri Hari Krishna Narayanan

Sarah Williamson

Differentiable Earth system models in Julia

Catalyst.jl is a modeling package for analysis and high performance simulation of chemical reaction networks (CRNs). It defines symbolic representations for CRNs, which can be created programmatically or specified via a domain specific language. Catalyst provides tooling to analyze models, and to translate CRNs to ModelingToolkit-based ODE, SDE, and jump process models. In this workshop we will overview how to generate, analyze, and efficiently solve such models across a variety of applications.

Samuel Isaacson

Torkel Loman

Modeling of Chemical Reaction Networks using Catalyst.jl

This minisymposium aims to provide researchers in astronomers and astrophysicists an opportunity to share how Julia has enhanced their science and the challenges they encountered.  We aim to identify shared needs (e.g., opportunities for new/upgraded packages) that could significantly accelerate the adoption of Julia among the astronomical research community.  A secondary goal is to help strengthen the community of Julia developers active in astronomical research.

Eric B. Ford

Julia in Astronomy & Astrophysics Research

DataFrames.jl provides a comprehensive set of functions that allow performing transformations of tabular data using an operation specification language. This language lets users pass columns from a source data frame, a function to apply to them, and column names to store the result in the target data frame. In this workshop, I will explain the functionalities that it provides. [Here](https://github.com/bkamins/JuliaCon2022-DataFrames-Tutorial) you can find workshop materials.

Bogumił Kamiński

A Complete Guide to Efficient Transformations of data frames

The "Julia for HPC" minisymposium aims to gather current and prospective Julia practitioners in the field of high-performance computing (HPC) from multidisciplinary applications. We invite participation from industry, academia, and government institutions interested in Julia’s capabilities for supercomputing. The goal is to provide a venue for Julia enthusiasts to share best practices, discuss current limitations, and identify future developments in the scientific HPC community.

Carsten Bauer

Michael Schlottke-Lakemper

Hendrik Ranocha

Johannes Blaschke

Jeffrey Vetter

Julia for High-Performance Computing

DFTK.jl is a framework for the quantum-chemical simulation of materials using Density Functional Theory. Many relevant physical properties of materials, such as interatomic forces, stresses or polarizability, depend on the derivatives of quantities of interest with respect to input data. To perform such computations efficiently Automatic Differentiation has been implemented in DFTK using both forward and backward modes of AD.

Fast Forward and Reverse-Mode Differentiation via Enzyme.jl

Purple

Markus Towara

Niklas Schmitz

Gaspard Kemlin

Automatic Differentiation for Quantum Electron Structure

The package VectorEngine.jl enables the use of the SX-Aurora Tsubasa Vector Engine (VE) as an accelerator in hybrid Julia programs. It builds on GPUCompiler.jl leveraging the VEDA API as well as the LLVM-VE compiler and the Region Vectorizer. Even though the VE is very different from GPUs, using only few cores and arithmetic units with very long vector length, the package enables programmers to use it in a very similar way, simplifying the porting of Julia applications.

Teaching GPU computing, experiences from our Master-level course

Blue

Erich Focht

Julia to the NEC SX-Aurora Tsubasa Vector Engine

This talk will present the package UnitJuMP that allows the user to include units when modelling optimization problems in JuMP. Both variables and constraints can have specified units, as well as parameters involved in objective and constraints. If different units are combined, functionality in Unitful is used to check for compatibility and perform automatic conversions.

SparseVariables - Efficient sparse modelling with JuMP

JuMP

Truls Flatberg

UnitJuMP: Automatic unit handling in JuMP

Could Julia be uniquely well-suited for rapidly developing new approaches to simulate the brain ? What if neuroscientists could use a composable set of tools to craft models of ion channels, compartmentalized neuronal morphology, networks of LIF or conductance-based neurons, reinforcement learning, and everything in-between?

Join the discussion on the [bof-voice](https://discord.com/channels/995757799076282478/997898697578913853) channel in discord.

Alessio Quaresima

Wiktor Phillips

Tushar Chauhan

Simulating neural physiology & networks in Julia

Is life possible without for-loops? This talk reviews some syntactic constructs available in Julia, especially the do-block, and relates them to programming language theory concepts.

Teaching with Julia (my experience)

Nicolau Leal Werneck

`do block` considered harmless

We present a Julia toolchain for the adaptive simulation of hyperbolic PDEs such as flow equations on complex domains. It begins with HOHQMesh.jl to create a curved, unstructured mesh. This mesh is then used in Trixi.jl, a numerical simulation framework for conservation laws. We visualize the results using Julia’s plotting packages. We highlight select features in Trixi.jl, like adaptive mesh refinement (AMR) or shock capturing, useful for practical applications with complex transient behavior.

CUPofTEA, versioned analysis and visualization of land science

Andrew Winters

From Mesh Generation to Adaptive Simulation: A Journey in Julia

I am tenure at the University and I teach several courses of Computer Science. In this talk of 10 minutes I will teach how I will my use of Julia as a useful resource for teaching. In this talk I will introduce how I have used Pluto/PlutoSliderServer to explain concept and to allow students to check some implementations. Also, I show an online web for creating easily online quiz for Moodle. Finally, I have used it to prototype some algorithms that later the students should implement.

Simulation of atmospheric turbulence with MicroHH.jl

Daniel Molina

Industry scale optimization problems are often large and sparse, and problem construction time can rival solution time. The default containers and macros in JuMP present some challenges for this class of problems, in particular some performance gotchas.

We present SparseVariables.jl for simple sparse modelling, and demonstrate performance and conciseness with a supply chain optimization example, benchmarking both problem construction time and LOC for multiple modelling approaches.

JuMP ToQUBO Automatic Reformulation

Lars Hellemo

We present ToQUBO.jl, a Julia package to reformulate general optimization problems into QUBO instances. This tool aims to convert JuMP problems for straightforward application in physics and physics-inspired solution methods whose normal form is equivalent to QUBO. It automatically maps between source and target models, providing a smooth JuMP modeling experience.
We also present a simple interface to connect various annealers and samplers as QUBO solvers bundled in another package, Anneal.jl.

A multi-precision algorithm for convex quadratic optimization

Pedro Xavier

Turbulence in the atmosphere is often studied using 3D simulations as a virtual laboratory. Most experiments require code modification by users, but this is hard, because most codes are written in low-level languages as Fortran and C++. MicroHH.jl, a Julia port of the dynamical core of MicroHH, has been designed to solve this problem. It is built on MPI.jl, LoopVectorization.jl, CUDA.jl, and HDF5.jl for the IO. Interaction with running simulations is made possible via user scripts.

ProtoSyn.jl: a package for molecular manipulation and simulation

Chiel van Heerwaarden

We present a GitHub organization, CUPofTEAproject, hosting a Franklin.jl website, cupoftea.earth, and a suite of Julia packages. The organization goal is to host versioned analysis and web interactive visualization (using WGLMakie.jl) of science studies about exchanges between terrestrial ecosystems and the atmosphere.

Alexandre A. Renchon

In this talk, we describe a Julia implementation of RipQP, a regularized interior-point method for convex quadratic optimization. RipQP is able to solve problems in several floating-point formats, and can also start in a lower precision as a form of warm-start. The algorithm uses sparse factorizations or Krylov methods from the Julia package Krylov.jl. We present an easy way to use RipQP to solve problems modeled with QuadraticModels.jl and LLSModels.jl.

Interior-point conic optimization with Clarabel.jl

Geoffroy Leconte

Enzyme is a new LLVM-based differentiation framework capable of creating fast derivatives in a variety of languages. In this talk we will showcase improvements in Enzyme.jl, the Julia-language bindings for Enzyme that enable us to differentiate through parallelism (Julia tasks, MPI.jl, etc), mutable memory, JIT-constructs, all while maintaining performance. Moreover we will also showcase Enzyme's new forward mode capabilities in addition to its existing reverse-mode features.

William Moses

Ludger Paehler

Tim Gymnich

ProtoSyn.jl is an open-source alternative to molecular manipulation and simulations software, built with a modular architecture and offering a clean canvas where new protocols and models can be tested and benchmarked. By delivering good documentation, ProtoSyn.jl aims to lower the entry barrier to inexperienced scientists and allow a “plug-and-play” experience when implementing modifications. Learn more on the project’s GitHub page:
https://github.com/sergio-santos-group/ProtoSyn.jl

PDDL.jl: A fast and flexible interface for automated planning

José Pereira

In the Fall Semester 2021 at ETH Zurich, we designed and taught a new course: **Solving PDEs in parallel on GPUs with Julia**. We present technical and teaching experiences we gained: we look at our tech-stack `CUDA.jl`, `ParallelStencils.jl` and `ImplictGlobalGrid.jl` for GPU-computing; and `Franklin.jl`, `Literate.jl`, `IJulia.jl`/Jupyter for web, slides, and exercises. We look into the crash-course in Julia, teaching software-engineering (git, CI) and project-based student evaluations.

Ludovic Räss

Samuel Omlin

Mauro Werder

The Planning Domain Definition Language (PDDL) is a formal specification language for symbolic planning problems and domains that is widely used by the AI planning community. This talk presents PDDL.jl, a fast and flexible interface for planning over PDDL domains. It aims to be what PyTorch is to deep learning, or what PPLs are to Bayesian inference: A general high-performance platform for contemporary AI applications and research programs that leverage automated symbolic planning.

Real-Time, I/O, and Multitasking: Julia for Medical Imaging

Xuan (Tan Zhi Xuan)

In this talk, we show how to use Julia to build the system software for a medical imaging device. Such a device is a distributed system that has to coordinate the handling of real-time signals and asynchronous tasks. The talk will highlight the key parts and design patterns of our software stack. We show how we used a variety of Julia features to implement the control logic of the entire imaging device and the coordination and communication with the large number of sub-devices it controls.

Build an extensible gallery of examples

Niklas Hackelberg

The talk will introduce Clarabel.jl, a conic convex optimization solver in pure Julia.   Clarabel.jl uses an interior point technique with a novel homogeneous embedding and can solve LPs, QPs, SOCPs, SDPs or exponential cone programs.   The talk will highlight the solver's performance advantages relative to competing solvers, discuss algorithmic and software design ideas drawn from existing solvers, and highlight extensibility features leveraging Julia's multiple dispatch system.

Paul Goulart

Examples are an essential part of the package documentation. In this talk, I'll introduce DemoCards.jl as a plugin package for Documenter to manage your demo files. I'll explain its design and show how it is used to build the demos in JuliaImages and JuliaPlots. Package authors and document writers are potential users of this package.

Johnny Chen

The accelerating outflow of ice in Antarctica or Greenland due to a warming climate or the geodynamic processes shaping the Earth share common computational challenges: extreme-scale high-performance computing (HPC) which requires the next-generation of numerical models, parallel solvers and supercomputers. We here present a fresh approach to modern HPC and share our experience running Julia on thousands of graphical processing units (GPUs).

Albert de Montserrat

GPU4GEO - Frontier GPU multi-physics solvers in Julia

In 2019 the Event Horizon Telescope (EHT) Collaboration produced the first image of a black hole. This talk details how Julia has been an essential tool for EHT black hole imaging and the advancement of black hole science. I will demonstrate how Julia’s features such as multiple dispatch, differentiable programming, and composability have enabled orders of magnitude performance improvement, moving black hole imaging from clusters to a single laptop.

Reaction rates and phase diagrams in ElectrochemicalKinetics.jl

Paul Tiede

Comrade: High-Performance Black Hole Imaging

JunctionTrees.jl implements the junction tree algorithm: an efficient method to perform Bayesian inference in discrete probabilistic graphical models. It exploits Julia's metaprogramming capabilities to separate the algorithm into a compilation and a runtime phase. This opens a wide range of optimization possibilities in the compilation stage. The non-optimized runtime performance of JunctionTrees.jl is similar to those of analog C++ libraries such as libdai and Merlin.

Automated Finite Elements: a comparison between Julia and C++

Martin Roa Villescas

JunctionTrees: Bayesian inference in discrete graphical models

JuMP 1.0 was released in March 2022. I'll present the state of JuMP today, how we got here, what the JuMP community should know about the 1.0 release, and what's next on the roadmap.

A user’s perspective on using JuMP in an academic project

Miles Lubin

JuMP 1.0: What you need to know

Using HawkesProcesses.jl I'll introduce the theory behind Hawkes process and show how it can be used across many different applications. Hawkes processes in Julia benefit from the speed of the language and composability  of the different libraries, as you can easily extend the Hawkes process using other packages without too much difficulty. Plus, by using Pluto notebooks I can build simple interactions that demonstrate the underlying mechanics of the Hawkes process.

Dithering in Julia with DitherPunk.jl

Dean Markwick

Using Hawkes Processes in Julia: Finance and More!

ModalDecisionTrees.jl offers a set of symbolic machine learning algorithms that extend classical decision tree learning algorithms, and are able to natively handle time series and image data. *Modal Decision Trees* leverage *modal logics* to perform a primitive-but-powerful form of entity-relation reasoning; this allows them to capture temporal and spatial patterns, and makes them suitable to natively deal (= no need for feature extraction) with data such as multivariate time-series and images.

Multivariate polynomials in Julia

Giovanni Pagliarini

ModalDecisionTrees: Decision Trees, meet Modal Logics

Julia Gender Inclusive is an initiative that supports gender diversity in the Julia community. Over the last year, we have worked toward doing so through meetups and workshops for community building and education. In this Birds-of-Feather session, we hope to discuss current and future initiatives with other people with underrepresented genders, as well as supportive allies.

Join the discussion on the [bof-voice](https://discord.com/channels/995757799076282478/997898697578913853) channel

Julia Gender Inclusive

Discussing Gender Diversity in the Julia Community

With Gridap, Julia has a Finite Element package that allows writing expressions that closely mimic the mathematical notation of the weak form of an equation and automate the assembly of the linear system from there. Rather than using macros, the equations are interpreted as regular Julia expressions. This approach is similar to what has been done in C++, e.g. in the Coolfluid 3 CFD code. In this talk, both methods will be compared, showing how Julia really is "C++ done right" for this use case.

Distributed AutoML Pipeline Search in PC/RasPi K8s Cluster

Bart Janssens

Dithering algorithms are a group of color quantization techniques that create the illusion of continuous color in images with small color palettes by adding high-frequency noise or patterns. Traditionally used in printing, they are now mostly used for stylistic purposes.

DitherPunk.jl implements a wide variety of fast and extensible dithering algorithms. Using its example, I will demonstrate how packages for creative coding can be built on top of the JuliaImages ecosystem.

FdeSolver.jl: Solving fractional differential equations

Adrian Hill

I will introduce ElectrochemicalKinetics.jl, a package that implements a variety of models for electrochemical reaction rates (such as Butler-Volmer or Marcus-Hush-Chidsey). It can also fit model parameters and construct nonequilibrium phase diagrams. While the package has already been of great use in electrochemical research applications, I will focus more on the design choices as well as the challenges that have come up in implementing automatic differentiation support.

RVSpectML:  Precision Velocities from Spectroscopic Time Series

Depending on the applications, the requirement for a multivariate polynomial library may be efficient computation of product, division, substitution, evaluation, gcd or even Gröbner bases. It is well understood that the concrete representation to use for these polynomials depends on whether they are sparse or not. In this talk, we show that in Julia, the choice of representation also depends on whether to specialize the compilation on the variables.

PHCpack.jl: Solving polynomial systems via homotopy continuation

Chris Elrod

Benoît Legat

Astronomers have detected nearly a thousand exoplanets by precisely charting the radial velocity (RV) wobble of their host stars.  The [RVSpectML](https://rvspectml.github.io/RvSpectML-Overview/) family of packages is a new, open-source, modular and performant pipeline for measuring radial velocities and stellar variability indicators from spectroscopic time-series.   This talk aims to give potential users and/or developers an overview of the component packages and their status.

State of JuliaGeo

In this lightning talk, I will present an example workflow in leveraging the Kubernetes cluster of RaspberryPis to perform parallel search in finding the best AutoML pipeline in a given classification task. While many applications of RasPis are targeted for IOT usage, a K8s cluster of RasPis running Julia can be targeted to solve more complex problems and I will provide examples of the cluster performance running AutoMLPipeline applications.

Comonicon, a full stack solution for building CLI applications

Paulito Palmes

FdeSolver Julia package solves fractional differential equations in the sense of Caputo, suitable for nonlinear and stiff ordinary differential systems. It has been used for describing memory effects in microbial community dynamics and complex systems. With some practical examples, I will present why (and how) developing such a computational package in open source programming is important (and useful).

Moein Khalighi

PHCpack is a software package for solving polynomial systems via homotopy continuation methods. Our interface exports the functionality of PHCpack either via the executable or the shared object file, via its C interface. The software is free and open source and we have a cloud server that hosts the application at phcpack.org.
Our talk will also explore a specific application area in the design of mechanisms.

A Tax-Benefit model for Scotland in Julia

Kylash Viswanathan

Jan Verschelde

The Risk-Aware Market Clearing (RAMC) project investigates the quantification and management of risk in power systems, thereby bridging machine learning, optimization and risk analysis. This talk will discuss the team's experience --from a user perspective-- on using Julia and JuMP within an academic project and a multi-disciplinary team. This will include the motivation for using these tools, as well as hurdles encountered along the way, and practical experience on industrial-size systems.

COPT and its Julia interface

Mathieu Tanneau

This talk discusses ScotBen, a microsimulation tax-benefit model for Scotland written in Julia. Scotben lets you analyse how changes to the tax system change revenues, inequality and poverty.

Bayesian Estimation of Macroeconomic Models in Julia

Graham Stark

In this talk, I will introduce Comonicon. Comonicon is a CLI generator designed for Julia, unlike other CLI generators such as Fire, ArgParse, and so on, Comonicon does not only parse command-line arguments but also provide a full solution for building CLI application (via PackageCompiler), packing tarballs, generating shell auto-completion, CLI application installation, mitigating CLI latencies. I'll also talk about ideas arise from development about the future official Julia application.

Cycles and Julia Sets: Novel algorithms for Numerical Analysis

Xiu-zhe (Roger) Luo

[JuliaGeo](https://juliageo.org) is a community that contains several related Julia packages for manipulating, querying, and processing geospatial geometry data. We aim to provide a common interface between geospatial packages. In 2022 there has been a big push to have parity with the Python geospatial packages, such as rasterio and geopandas. In this 10 minute talk, we'd like to show these improvements---both in code and documentation---during a tour of the geospatial ecosystem.

Towards Using Julia for Real-Time applications in ASML

Josh Day

Rafael Schouten

Maarten Pronk

If you want to simulate something, sooner or later you’re going to come across partial differential equations. But solving PDEs is hard, right? It doesn’t have to be! In this talk we'll cut to the chase: how do I copy paste the textbook description of my PDE into Julia symbolic syntax and get a solution? MethodOfLines.jl is the answer, and in this talk we'll show you how to do it!

Alex Jones

Automated PDE Solving in Julia with MethodOfLines.jl

ASML is a 30.000+ employee company which is the world leader on photo-lithographic systems that are crucial for semiconductor manufacturing. During the last two years a community of Julia enthusiasts has been running pilot projects to assess opportunities offered by Julia for rapid development of early Proof-of-Concepts and, subsequent, rapid deployment in prototypes and, whether possible, actual products. Similar to other robotic systems, ASML lithography systems have hard real-time....

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