Integrated Assessment Models (IAMs) are scientific models which enable the evaluation of the social and economic impact of environmental policies. WorldDynamics.jl is an open-source package that allows the development and reproduction of modern and historical IAMs in Julia. In this workshop, we demonstrate the main functionalities of the package while guiding the audience with comprehensive hands-on examples.

Lunch Workshop Day (Room 4)

32-144

Paulo Bruno Serafim

Integrated assessment modeling using WorldDynamics.jl

Come and help us circulate the love for lumped parameter modelling with CirculatorySystemModels.jl, a fast, friendly, flexible and functional circulatory modelling framework. In this workshop participants will be helped to run and analyze a lumped parameter model, embodying the statement "if you can draw it you can model it". We will guide users through the specifics of how they can then begin to develop their own advanced features utilizing the dependent package ModelingToolkit.jl.

Lunch Workshop Day (Room 3)

32-D463 (Star)

Torsten Schenkel

Harry Saxton

Hands on lumped parameter models with CirculatorySystemModels.jl

Why to wait hours for computations to complete, when it could take only a few seconds? Tired of prototyping code in an interactive, high-level language and rewriting it in a lower-level language to get high-performance code? Unsure about the applicability of differentiable programming? Or simply curious about parallel and GPU computing and automatic differentiation being game changers in physics-based and data-driven modelling.

Lunch Workshop Day (Room 2)

32-123

Ludovic Räss

Samuel Omlin

Ivan Utkin

Differentiable modelling on GPUs

The objective of this workshop is to give exposure for using various digital image processing methods in Julia. JuliaImages hosts the major Julia packages for image processing in which Images.jl provides access to various algorithms acting as the umbrella package. The applications of Image processing are found in areas such as machine vision, biomedical image processing and remote sensing. This workshop will help the Julia enthusiasts/scholars to get trained in Julia based image processing .

Lunch Workshop Day (Room 1)

32-G449 (Kiva)

Tim Holy

Ashwani Rathee

Image Processing with Images.jl Workshop

We hope you're enjoying JuliaCon 2023 so far! Take a break and grab some lunch to recharge for the afternoon sessions. We have a delicious spread waiting for you in the dining hall. Bon appétit!

Using NeuralODEs in real life applications

Interactive Data Dashboards with Genie: Design to Deployment

Building REST APIs with Julia

Working with DataFrames.jl beyond CSV files

Although Julia is a growing, fast language with relatively easy syntax, and thus quite a productive language, its popularity still seems enormously lopsided towards the scientific community. Thus it can be quite an intimidating language and community for newbies without heavy or good scientific or mathematical background to get into.

To address the intimidation above, I would like to propose this workshop, where we'll build and deploy a REST project for the web, a ubiquitous technology.

JAKE

Data scientists need to work with various data sources and sinks in their projects. During the workshop I you will learn how you can work with standard data formats using DataFrames.jl. A special focus will be put on working with data that is larger than available RAM.

Bogumił Kamiński

Jacob Quinn

NeuralODEs lead to amazing results in academic examples. But the expectations are often being disappointed as soon as one tries to adapt this concept for real life use cases. Bad convergence behavior, handling of discontinuities and/or instabilities are just some of the stumbling blocks that might pop up during the first steps. During the workshop, we want to show how to integrate real life industrial models in NeuralODEs using FMI and present sophisticated training strategies.

Tobias Thummerer

Lars Mikelsons

Learn how to create production ready data applications and interactive data exploration dashboards in Julia, using Genie.

Adrian Salceanu

Pere Giménez

Opening Ceremony

26-100

Breakfast in Stata Center

Welcome to the opening ceremony of JuliaCon 2023! Join us as we kick off this exciting conference with a warm welcome, keynote speeches, and announcements about what's in store for the week. Get ready to meet new colleagues, exchange ideas, and discover the latest in Julia programming. Let's get started!

Keynote: Tim Davis

Timothy A. Davis, PhD, is a Professor in the Computer Science and Engineering Department at Texas A&M University, and a Fellow of SIAM, ACM, and IEEE. He serves as an associate editor for the ACM Transactions on Mathematical Software. In 2018 he was awarded the Walston Chubb Award for Innovation, by Sigma Xi.

Keynote: Christopher Rackauckas

We are interested in exploring different aspects how Julia programming language can be applied to certain interesting usecases in the actuarial and financial space. In addition we may also provide commentary on when to use certain languages in terms of productivity and performance.

Pipelines & JobSchedulers for computational workflow development

Online talks and posters

Yun-Tien Lee

Julia usecases in actuarial science related fields

We demonstrate a set of pure Julia packages to interactively reconstruct and render Neural Radiance Fields (NeRFs), a 3D neural representation of a scene from pictures, using heterogeneous kernel programming. This system was developed at AMD and supports other GPU backends as well.

Anton Smirnov

Nerf.jl a real-time Neural 3D Scene Reconstruction in pure Julia

Computational workflows and pipelines are essential in large-scale data analysis and bioinformatics. Here, we present Pipelines.jl and JobSchedulers.jl, as the reproducible and scalable pipeline builder and workload manager. Pipelines.jl provides simple but powerful methods to wrap Julia function or external command, and provide data and dependency validation, and job re-try or skipping. JobSchedulers.jl can allocate CPU and memory to jobs and defer a job until other jobs are finished.

Ignite.jl: A brighter way to train neural networks

Jiacheng Chuan

Li Sean

We demonstrate how to use NCI services to run and profile Julia code at scale.

Yue Sun

Julia at NCI

Dr. Rackauckas is a Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology, VP of Modeling and Simulation at JuliaHub and Creator / Lead Developer of JuliaSim. He's also the Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas, and Lead Developer of the SciML Open Source Software Organization.

JuliaHub sponsor talk

We introduce Ignite.jl, a package that streamlines neural network training and validation by replacing traditional for/while loops and callback functions with a powerful and flexible event-based pipeline. The key feature of Ignite.jl is the separation of the training step from the training loop, which gives users the flexibility to easily incorporate events such as artifact saving, metric logging, and model validation into their training pipelines without having to modify existing code.

Fast Higher-order Automatic Differentiation for Physical Models

Jonathan Doucette

This talk covers NLP analysis with Julia, with a focus com graph analysis.

A basic pipeline is shown, including pre-proccessing graph and semantic embeddings with TextGraphs.jl.

We briefly study the insides of this package along with an use case. For this, the heteronyms of Portuguese poet Fernando Pessoa are contrasted with each other. We see how speech structural properties reflect different writing styles.

Felipe Coelho Argolo

NLP and human behavior with Julia (and a bit of R)

Pre-recorded talks will be played in a playlist at 32-082 on Wednesday from 10 AM - 4 PM.

32-082

Pre-Recorded Talks

We discuss the ctrl-VQE algorithm, a variant of the popular Variational Quantum Eigensolver for solving molecular electronic states which has the potential to reduce runtimes on a quantum computer by orders of magnitude. Our most recent implementation of ctrl-VQE using Julia allows us to simulate our algorithm much faster than our previous Python code, enabling a more thorough analysis which has revealed strategies to improve the runtime of the algorithm even more.

Kyle Sherbert

ctrl-VQE: Julianic simulations of a pulse-level VQE

Quantum computing may be the technology that will change the financial industry. One of the potential use cases for quantum computers is derivative instrument pricing, this is a computationally demanding task with proven quadratic speed-up on the quantum machine. 

The goal of the talk is to show how to do this using Julia's library  QuantumCircuits.jl.

Lessons learned while working as a technical writer at FluxML

Rafał Pracht

Quantum Monte Carlo in Julia.

Evolutionary relationships are often depicted in tree structures where internal nodes represent ancestral species and external nodes represent extant species, but directed networks are often necessary. Many species tree and network estimation procedures first estimate a set of gene trees from sequence data. The proportion of these gene trees that do not necessitate a network model is biologically interesting. In RANSANEC.jl, we expand random sample consensus to tree-space to tackle this problem.

Streaming real-time financial market data with AWS cloud

Nathan Kolbow

Sorting gene trees by their path within a species network

Taking higher-order derivatives is crucial for physical models like ODEs and PDEs, and it would be great to get it done by automatic differentiation. Yet, existing packages in Julia either has exponential scaling w.r.t. order (nesting first-order AD) or has exponential scaling w.r.t. dimension (nested Taylor polynomial in TaylorSeries.jl). The author presents TaylorDiff.jl (https://github.com/JuliaDiff/TaylorDiff.jl) which is specifically optimized for fast higher-order directional derivatives.

ModelOrderReduction.jl -- Symbolic-Enhanced Model Simplification

Songchen Tan

Sponsor talk of JuliaHub, platinum sponsor at JuliaCon.

ASML & Julia

We present a JuMP-based solver that combines a nested primal-dual decomposition technique and convex relaxation approaches for tackling non-convex multi-stage stochastic programming problems. The approach addresses optimal long-term water supply infrastructure planning with feasibility constraints at operational timescales. We combine an outer primal decomposition of planning stages and inner dual ones of operating scenarios, with convexified non-anticipative constraints relaxed for scenarios.

Alireza Shefaei

Edo Abraham

Nested approaches for multi-stage stochastic planning problems

VLLimitOrderBook.jl is a package written in Julia that simulates order book dynamics and matching for equities, options, and cryptocurrency orders. The orders in the book are stored in an AVL Tree data structure and prioritized based on price and time.

Ruize Ren

VLLimitOrderBook.jl, simulation of electronic order book dynamic

Thank you to ASML, gold sponsor at JuliaCon 2023!

Working as a technical writer at FluxML for the past six months has taught me some tips and tricks for writing and maintaining user-centric documentation. Documentation not only requires writing clear and concise content but also building infrastructures, testing, and a lot of discussions. This talk takes all the lessons I learned (myself and through FluxML maintainers) in the past six months and condenses it into 3 minutes!

Saransh Chopra

Unlike in Online RL where agents need to interact with real environment, Offline RL works similar to a typical machine learning workflow. Given a dataset, Offline RL processes data extracting state, action, reward, and terminal columns to optimize the policy Q.  By wrapping up offline RL into the AutoMLPipeline workflow, it becomes trivial to search for the optimal preprocessing elements and their combinations to improve Offline RL optimal policy using symbolic workflow manipulation.

Vahana.jl - A framework for large-scale agent-based models

Paulito Palmes

Wrapping Up Offline RL as part of AutoMLPipeline Workflow

Historical data is useful in the financial industry, particularly for back-testing trading strategies. However, a single machine can't store real-time data for even a single ticker symbol. Thus, constructing a distributed scalable computer system to persist real-time financial data requires time and a significant financial commitment. Using cloud computing services reduces this barrier. This talk describes a real-time cloud-based financial data streaming and storage system implemented in Julia.

We present RxInfer.jl, which is a Julia package for automated and fast Bayesian inference in a probabilistic model through reactive message passing on a factor graph representation of that model. RxInfer.jl unites different Julia packages and forms a user-friendly ecosystem for efficient real-time Bayesian processing of infinite data streams.

Dmitry Bagaev

RxInfer.jl: a package for real-time Bayesian Inference

Vahana.jl is a new open-source high-performance software framework for development of large-scale agent-based models (ABM) of complex social systems.

Vahana models are expressed as synchronous graphic dynamical systems (SyGDS). This allows Vahana to run a simulation in parallel and to distribute the simulation across multiple nodes of a cluster. New extensions to the traditional SyGDS approach allow the implementation of complex ABMs.

Steffen Fürst

This talk will present some Julia tools and features that helped an apprentice research software engineer (and newcomer to Julia) adopt good software engineering practices.

Branwen Snelling

Becoming a research software engineer with Julia

We present ModelOrderReduction.jl -- a Julia package for the automatic simplification of scientific models by way of symbolic computation. As such, ModelOrderReduction.jl enables modelers without detailed knowledge of the domain of model order reduction to automatically generate simplified models that approximate the dominant behavior of otherwise too complex ones.

Flemming Holtorf

Bowen S. Zhu

Presentation of results from this year's Julia Developer Survey and discussion of progress made.

Morning Break Day 1 Room 7

Andrew Claster

Julia Developer Survey Results

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Attitude control subsystem development using Julia

Morning Break Day 1 Room 4

Quantum Minis Introduction

Morning Break Day 1 Room 6

Patterns for portable parallelism: porting CliMA to GPUs

Third Millennium Symbolic Learning with Sole.jl

Morning Break Day 1 Room 2

Morning Break Day 1 Room 5

Debugging time spent in LLVM with SnoopCompile

32-141

Morning Break Day 1 Room 1

MRI Compressed Sensing and Denoising in Julia

32-124

Morning Break Day 1 Room 3

Compressed sensing is an area of mathematics increasingly being applied in magnetic resonance imaging. MRI imagery is highly sparse and noisy making it an appropriate application to use these techniques. Compressed sensing complements existing techniques such as JPEG compression in computationally intensive applications such as MRI image processing to reduce acquisition time, in AI training time and can be used to build more robust systems with greater resilience to noise.

Exploring the State of Machine Learning for Biological Data

Alexander Leong

The development of a satellite attitude and orbit control subsystem (AOCS) imposes challenges since the dynamics cannot be reproduced on the ground. The entire process relies on simulation. We developed a novel workflow that improved productivity by using the outstanding adaptability of the Julia language and its ecosystem. We could adapt a validated simulator to test and verify all the AOCS algorithms at each development phase, drastically reducing error propagation and, consequently, test cost

DyVE, a Framework for Value Dynamics

Ronan Arraes Jardim Chagas

Despite many efforts, it can be difficult to find good abstractions that are efficient on both CPU and GPU code. In our effort to add GPU support to ClimaCore.jl, we have established with several useful patterns for describing common spatial operations at a high-level, which can then be specialized in different ways to different computational backends.

HDF5.jl: Hierarchical data storage for the Julia ecosystem

Simon Byrne

[Sole.jl](https://bit.ly/3X5pQUJ) is a framework for *symbolic learning*, i.e., machine learning with symbolic logic.
It comprehends packages for:
- Computational logic ([SoleLogics.jl](https://bit.ly/3QwLRJF));
- Operating with multimodal (un)structured data ([SoleData.jl](https://bit.ly/3GYh4SF), [SoleFeatures.jl](https://bit.ly/3GVDNPj));
- Learning, inspecting and analyzing symbolic models ([SoleModels.jl](https://bit.ly/3GVDPqp), [SolePostHoc.jl](https://bit.ly/3QAIL7b)).

What's new with Progradio.jl - Projected Gradient Optimization

Giovanni Pagliarini

SnoopCompile has a feature of timing the time spent in LLVM to optimize Julia code. This is a talk sharing my experiences using the macro snoopl to debug the time spent optimizing each function in LLVM.

Pinning Julia Threads to CPU-Cores with ThreadPinning.jl

Guilherme Bodin

In recent years the Julia language has seen increasing adoption in the quantum community, broadly defined. We propose to organize two back-to-back minisymposia on the theme of "Julia and quantum," including quantum computing, condensed matter physics, quantum chemistry. The talks would be addressed to both current and future experts.

Qurt.jl: Compiling Quantum Circuits (in Julia)

I'll present Qurt.jl, a package for creating and transforming quantum circuits. I'll do a
brief demo, including the obligatory, slick, macro-enabled builder interface. I'll show how
it is faster than some Python-Rust and C++ implementations.

Convenient time dependence in QuantumOptics.jl

John Lapeyre

Have you ever wondered how to bind a thread to a specific CPU-core in Julia? And why that might be useful in the first place? Then this talk is for you!  I demonstrate how to easily query and control the affinity of your Julia threads using ThreadPinning.jl and present instructive examples that highlight the importance of thread pinning, especially on HPC clusters.

Shipping Julia Packages with System-Images

Carsten Bauer

With a low barrier to entry and a large ecosystem of tools and libraries that allow quick prototyping, Julia has great potential for geospatial development. SARProcessing.jl is a much open-source project with the aim of making SAR data processing easy and fast for everyone. This talk provides a gentle hands-on introduction to setting up and enjoying the SARProcessing.jl package.

JuliaEO 2023: Outcomes, Overview & Impact

Iga Szczesniak

Kristian Aalling Sørensen

SARProcessing.jl: A flexible package for the SAR data processing

The Julia ecosystem has super fast and performant packages, but not always upon first use. This may be alleviated using system images and this talk presents a convenient workflow to ship them. Additionally, it discusses various challenges when doing so and shares our experience in reliably tackling them.

IsDef.jl: maintainable type inference

Joris Kraak

Venkatesh Prasad

QuantumOptics.jl is a numerical framework for simulating quantum systems. It provides a very general set of tools that go far beyond quantum optics. We recently added "time dependent operators", which allow easy creation and composition of time-dependent Hamiltonians and Lindbladians for use inn dynamical simulations. This talk briefly introduces the QuantumOptics.jl and these convenient new time-dependence features.

Braket.jl: A Julia SDK for Quantum Computing

Ashley Milsted

Exploring the use of Julia, in analyzing biological data. Discussion of libraries and packages, challenges and opportunities of using machine learning on biological data, and examples of past and future applications.

Lunch Day 1 (Room 3)

Edmund Miller

DyVE (Dynamics of Value Evolution) is an open source framework, aimed at designing, fitting, and integrating complex real-world process models; accounting for the accrual of costs and rewards; and supporting complex decision making, particularly in pharmaceutical R&D and business.
 
Process specifications are compact and algebraically composable. Uncertainty about model values or structure is supported. DyVE is interoperable with Scientific Machine Learning (SciML).

Lunch Day 1 (Room 4)

Jan Bima

Otto Ritter

Sean L Wu

IsDef.jl provides maintainable type inference in that it
1. Uses code generation where possible to deterministically infer the types of your code
2. Allows you to overload type-inference with your custom implementation
3. If neither code generation works nor a custom type inference rule is given, it falls back to Core.Compiler.return_type, wrapped by some safety nets

In this talk IsDef.jl is presented, along with typical applications and details about the implementation.

Lunch Day 1 (Room 1)

Stephan Sahm

Lunch Day 1 (Room 6)

Katharine Hyatt

Box-constrained optimization problems are ubiquitous in many areas of science and engineering. Our package includes methods tailored to this class of optimization problems. Due to Julia's `Iterator` interface, Progradio's solvers can be paused, resumed, or terminated early. Since the first release, we have included a stricter line-search procedure, and support for simplex constraints (Σ x_j = 1). Progradio's unique features make it attractive to be used as a sub-routine for dynamic optimization.

Eduardo M. G. Vila

HDF5.jl is a Julia package for reading and writing data using the Hierarchical Data Format version 5 (HDF5) C library. HDF5 is a flexible, self-describing format suitable for storing complex scientific data, and is used as a container for many other formats. 
This talk will give an overview of the HDF5 format and give an introduction and examples of basic usage of the HDF5.jl package. We will highlight some recent features and discuss future plans for the package.

Lunch Day 1 (Room 7)

Mustafa Mohamad

Mark Kittisopikul, Ph.D.

A community of Julia developers working with Earth Observation was brought together at the _JuliaEO2023: Global Workshop on Earth Observation with Julia_. 300 hundred people registered and 40 attended in person. All major aspects were covered: big geospatial data, remote sensing, data processing, visualization, modelling, data science, machine learning, artificial intelligence, and cloud computing. A Docker container and a Dataverse archive complement the notebook collection for reproducibility.

Lunch Day 1 (Room 5)

Gael Forget

Joao Pinelo

Adriano Coutinho de Lima

Andre Valente

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

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