JuliaCon 2022 Highlights
This year’s JuliaCon was the biggest and best ever with more than 50 thousand unique viewers on YouTube and other platforms. All 229 presentations, workshops and other videos are available on YouTube.
This year’s highlights include:
State of Julia (Dr. Jeff Bezanson): new compiler developments, language features, roadmap for future developments
Julia Computing sponsor talk (Dr. Viral Shah, Dr. Chris Rackauckas, Dr. Elisabeth Roesch, Glen Hertz, Dr. Matt Bauman): JuliaSim, PumasQSP, Cedar EDA, JuliaHub
APIs and Community: Building for Success (Dr. Erin LeDell, Chief Machine Learning Scientist at H2O.ai)
What Makes a Programming Language Successful (Jeremy Howard, Founding Researcher at Fast.ai)
Modeling and Simulation to Guide Dose Selection for mRNA Therapeutics and Vaccines (Dr. Husain Attarwala, Head of Clinical Pharmacology and Pharmacometrics at Moderna)
Julia User & Developer Survey 2022 (Andrew Claster)
And congratulations to the winners of the 2022 Julia Community Prizes!
Dr. Morten Piibeleht, Michael Hatherly, Dr. Fredrik Ekre, and Dr. Mauro Werder for their work on Documenter.jl and its ecosystem
Dr. Frames White for her many technical and community contributions across the Julia ecosystem
Shuhei Kadowaki for his work on JET.jl and the Julia compiler
For more highlights, see JuliaCon 2022 Highlights from the JuliaCon 2022 Organizing Committee.
PumasQSP v2.0: PumasQSP is a comprehensive cloud-based platform for quantitative systems pharmacological analytics. PumasQSP v2.0 is now available with new and improved features:
Bayesian inference is now available when generating virtual populations
The timespan of a given trial can be fit to data, similarly to how model parameters and initial conditions are optimized to match the data of the trial.
Subsampling a virtual population of patients vp for a given trial can now be performed with a simple call to subsample(alg, vp, trial), where alg is one of several available subsampling methods, like MAPEL or Allen-Rieger-Musante 2016 ARM.
Users can now set target distributions for any number of model states and our new custom-made method will subsample a virtual population, so that the same model states of virtual patients match the target distributions, as measured by discretized histograms.
JuliaHub v5.7.1: JuliaHub v5.7.1 is now available. JuliaHub is the entry point for all things Julia: explore the ecosystem, build packages and deploy a supercomputer at the click of a button. JuliaHub also allows you to develop Julia applications interactively using a browser-based IDE or by using the Pluto notebook environment and then scale workloads to thousands of cores. Version 5 features a brand new user interface, reduced app startup latency, and many more usability enhancements. JuliaHub is the easiest way to start developing in Julia or share your work using dashboards and notebooks. JuliaHub v5.7.1 contains the following new features:
CPU and Memory usage of jobs are now available as charts
Offline package server
New application: Windows Desktop
New feature: Project (Experimental)
JuliaSim v0.22.0: JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in SciML with equation-based digital twin modeling and simulation. JuliaSim v0.22.0 is now available with the following features:
The Functional Mockup Unit (FMU) Accelerator Graphical User Interface (GUI) provides a browser-based no-code workflow for generating surrogates of FMUs. It includes support for linear and non-linear continuous-time echo stem network (CTESN) and corresponding configuration options (i.e. reservoir size, number of sample points and time span to simulate over)
JuliaSimSurrogates.jl supports parallelized generation of simulation data that scales with available compute for ModelingToolKit.jl models and FMUs for Model Exchange/CoSimulation.
JuliaSimControls.jl includes Autotuning of proportional-integral (PI) / proportional-integral-<wbr>derivative (PID) controllers and a Pluto-based GUI for autotuning, with:
Linear constrained model predictive control (MPC) functionality
Nonlinear MPC functionality using sequential quadratic programming (SQP)
A library of state observers, including Kalman filter, Extended and Unscented Kalman filter (EKF/UKF)
Linear analysis of ModelingToolkit models through frequency-response analysis
Tuning-objective types with plot recipes
Inverse-optimal control for robust MPC tuning
Linear model reduction and controller-order reduction
Pluto-based GUI for model reduction
DFTSurrogates.jl provides a set of surrogates for computational chemistry:
Reading and writing of crystallographic interchange format (CIF) / XYZ / simplified molecular-input line-entry system (SMILES) format
Compatibility with MolecularGraph.jl for graph representation, Xtals.jl for representing crystals and AtomsBase.jl for generic atomic properties
Generating featurization pipelines based on atomic, pair and bond properties
Surrogates by Crystal Graph Convolutional Neural Networks
Parallelization of inference for multiple inputs candidate molecules
Ability to train surrogates with custom architectures and datasets
Interoperability with Chemellia
Mathematical Programming with Julia: Mathematical Programming with Julia is a new book by Richard Lusby and Thomas Stidsen. It features an open source approach to linear and mixed-integer programming. Click here for more.
Julia for Election Forecasting: Do you know a media company interested in covering the intersection of politics and statistics? The TuringElect team is looking for media partners interested in Bayesian forecasts for the upcoming midterms! More information is available here, and you can also find a sneak preview of what their model can do. Contact email@example.com for more details.
Julia for Power Dynamics: Continuous-Time Echo State Networks for Predicting Power System Dynamics is a new paper co-authored by Dr. Chris Rackauckas, Julia Computing Director of Modeling and Simulation. “Continuous-time echo state networks (with hybrid terms) can accurately predict the (highly stiff) dynamics of power system dynamics,” Chris explains. The paper is based on work with Lawrence Livermore National Laboratory on surrogates of differential-algebraic equation (DAE) systems. Click here for more.
Julia for Power Grid Optimization: Society for Industrial and Applied Mathematics (SIAM) News has published Rapid Prototyping with Julia: From Mathematics to Fast Code which explains how Julia is used for power grid optimization with ExaSGD as part of the US Department of Energy’s Exascale Computing Project (ECP). Click here for more.
Julia for Market Prediction: “G-Research is looking for a software engineer who is keen to contribute directly to the open-source Julia project. G-Research uses data science and machine learning tools to predict movements in the markets and we're very interested in furthering the development of the Julia language and supporting the community. This role could focus on a number of different areas from the compiler to Flux to improved packaging to DataFrames – there's a lot to do and we're looking for someone who has a passion to move an area of Julia forward. This role will be a part of our open-source program office so all contributions from this role will definitely impact the entire community. If you're interested, reach out at firstname.lastname@example.org"
Julia Computing - Coming to a Conference Near You: Julia Computing will be present at a number of upcoming conferences and events. Click below for more information.
Cambridge, MA: Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous Time Echo State Networks as Implicit Machine Learning at Foundations of Systems Biology in Engineering with Dr. Chris Rackauckas (Julia Computing) Aug 28-31
New Orleans: Conference on Neural Information Processing Systems (NeurIPS) with Julia Computing Nov 28-Dec 9
Converting from Proprietary Software to Julia: Are you looking to leverage Julia’s superior speed and ease of use, but limited due to legacy software and code? Julia Computing and our partners can help accelerate replacing your existing proprietary applications, improve performance, reduce development time, augment or replace existing systems and provide an extended trusted team to deliver Julia solutions. Leverage experienced resources from Julia Computing and our partners to get your team up and running quickly. For more information, please contact us.
Careers at Julia Computing: Julia Computing is a fast-growing tech company with fully remote employees in 12 countries on 5 continents. Click here to learn more about exciting careers and internships with Julia Computing.
Julia and Julia Computing in the News
Proceedings of the National Academy of Sciences: Efficient Computation of N-Point Correlation Functions in D Dimensions
Analytics Insight: Why Python Alone Will Make You Fail in Data Science
CIO Insight: 5 of the Best Machine Learning Tools in 2022
Dataconomy: Best AI Programming Languages in 2022
Nature - Scientific Reports: Almost Complete Solution for the NP-Hard Separability Problem of Bell Diagonal Qutrits
Unite.ai: 5 Best Machine Learning (AI) Programming Languages
Open Source For U: Artificial Intelligence: Explaining the Basics
Julia Blog Posts
JuliaCon 2022 Highlights (JuliaCon 2022 Organizing Committee)
Strings vs. Symbols in DataFrames.jl Column Indexing (Bogumił Kamiński)
How to Safely Use the Vec and Reshape Functions in Julia? (Bogumił Kamiński)
How DataFrames.jl Helps Fighting Piracy (Bogumił Kamiński)
Getting Ready for JuliaCon 2022 (Bogumił Kamiński)
Why Do I Use Main Function in My Julia Scripts? (Bogumił Kamiński)
Efficiency of Data Frame Row Iteration (Bogumił Kamiński)
Pandas-Like DataFrame Filtering in Julia (Emmett Boudreau)
Julia Functions in Excruciating Detail (Emmett Boudreau)
Build a Web-Based Markdown Editor in Julia in Less than 5 Minutes (Emmett Boudreau)
Toolips Uploader!: Get Files from Client to Server with Outrageous Ease (Emmett Boudreau)
Speed Up Your Code 100x in 30 Seconds: Break and Continue (Emmett Boudreau)
Five Critical Julia Looping Tips (Emmett Boudreau)
Toolips 0.1.5: Better Servers, Extendable Connections, Better Docs (Emmett Boudreau)
ToolipsApp.jl: My New Project Endeavor (Emmett Boudreau)
This Julia Method Trick Has Mindblowing Results (Emmett Boudreau)
How to Create Exceptions in Julia (Emmett Boudreau)
Julia IS General Purpose (Emmett Boudreau)
Should We Just Stop Using For Loops in Julia? (Emmett Boudreau)
Broadcasting POWER in Julia: Beginner Friendly Overview (Emmett Boudreau)
Things to Know Before Using Julia for Machine-Learning (REVISITED) (Emmett Boudreau)
Julia Has Unparalleled Extensibility (Emmett Boudreau)
7 Important Julia Methods to Extend (Emmett Boudreau)
Another Awesome Toolips.jl Update (0.2-pre) (Emmett Boudreau)
How to Make Declarative Measurement Syntax in Julia (Emmett Boudreau)
7 Immaculate Ways to Manipulate Julia (Emmett Boudreau)
Creating a Voiceover Portfolio with Toolips.jl (Part Two) (Emmett Boudreau)
CarouselArrays.jl — Repeating Arrays in Julia (Emmett Boudreau)
10 Advanced Tips for New Julia Programmers (Emmett Boudreau)
Toolips 0.2 Is Done; Olive Is Coming (Emmett Boudreau)
Optimising FPL with Julia and JuMP (Dean Markwick)
Machine Learning Property Loans for Fun and Profit (Dean Markwick)
Programmers' Preferences for Package Names (Dheepak Krishnamurthy)
Quick Intro to the New Effect Analysis of the Julia Compiler (Shuhei Kadowaki)
Recent Julia Online Events
Pittsburgh: Society for Industrial and Applied Mathematics (SIAM) with Julia Computing Jul 11-15
Virtual Workshop: Joint Automated Repository for Various Integrated Simulations (JARVIS) - National Institute of Standards and Technology (NIST) - Artificial Intelligence for Materials Science (AIMS) with Dr. Chris Rackauckas (Julia Computing) Jul 12-14
Virtual Conference: Improving Forecasting by Merging Deep Learning with Mechanistic Modeling at 8th Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability and Applications with Dr. Chris Rackauckas (Julia Computing) Aug 15
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About Julia Computing and Julia
Julia Computing's mission is to develop products that bring Julia's superpowers to its customers. Julia Computing's flagship product is JuliaHub, a secure, software-as-a-service platform for developing Julia programs, deploying them, and scaling to thousands of nodes. It provides the power of a supercomputer at the fingertips of every data scientist and engineer. In addition to data science workflows, JuliaHub also provides access to cutting-edge products such as Pumas for pharmaceutical modeling and simulation, JuliaSim for multi-physics modeling and simulation, and Cedar for electronic circuit simulation, combining traditional simulation with modern SciML approaches.
Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded by users at more than 10,000 companies and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software and the 2019 Sidney Fernbach Award. Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.