Newsletter September 2021 - Julia Will Be Featured in the Modelica Conference Keynote Address

Julia Will Be Featured in the Modelica Conference Keynote Address: On Monday September 20, Julia Computing CEO Viral Shah and Director of Modeling and Simulation Chris Rackauckas will be joined by Chris Laughman (Mitsubishi Electric Research Laboratories) to present the keynote address at this year’s Modelica conference.

• Keynote Address: New Horizons in Modeling and Simulation in Julia

• Presenters: Viral Shah (Julia Computing, CEO and Co-Founder), Chris Rackauckas (Julia Computing, Director of Modeling and Simulation and Christopher Laughman (Mitsubishi Electric Research Laboratories, Principal Member Research Staff)

• Modelica Conference: Link

• Date and Time: Monday September 20 at 8:15 am Eastern time (US) / 2:15 pm Central European Summer Time

• Abstract: As modeling has become more ubiquitous, our models keep growing. The time to build models, verify their behavior, and simulate them is increasing exponentially as we seek more precise predictions. How will our tools change to accommodate the future? Julia's language design has led to new opportunities. The combination of multiple dispatch, staged compilation, and Julia's composable libraries have made it possible to build a next generation symbolic-numeric framework. Julia's abstract interpretation framework enables capabilities such as automatic differentiation, automatic surrogate generation, symbolic tracing, uncertainty propagation, and automatic parallelism. These features have allowed us to build various applications in pharmaceuticals, aerospace, energy, materials, circuits, and much more - demonstrating performance and productivity that are many orders of magnitude better.

Other Julia presentations at this year’s Modelica conference include:

• NeuralFMU: Towards Structural Integration of FMUs into Neural Networks - Tobias Thummerer, Lars Mikelsons and Josef Kircher

• Modia - Equation Based Modeling and Domain Specific Algorithms - Hilding Elmqvist, Martin Otter, Andrea Neumayr and Gerhard Hippmann

• Modia and Julia for Grey Box Modeling - Frederic Bruder and Lars Mikelsons

• Composing Modeling and Simulation with Machine Learning in Julia - Chris Rackauckas, Ranjan Anantharaman, Alan Edelman, Shashi Gowda, Maja Gwozdz, Anand Jain, Chris Laughman, Yingbo Ma, Francesco Martinuzzi, Avik Pal, Utkarsh Rajput, Elliot Saba and Viral Shah

• OpenModelica.jl: A Modular and Extensible Modelica Compiler Framework in Julia Targeting ModelingToolkit.jl - John Tinnerholm, Adrian Pop, Andreas Heuermann and Martin Sjölund

Julia Computing and Pumas-AI at Population Approach Group Europe (PAGE): Julia Computing and Pumas-AI presented at the annual Population Approach Group Europe (PAGE) conference. Julia Computing and Pumas-AI presentations include:

• Stochastic Approximation Expectation Maximization (SAEM) in Pumas - Chris Elrod (presenter), Andreas Noack, Patrick Mogensen, Vaibhav Dixit, Vijay Ivaturi

• Generalized First-Order Conditional Estimation (FOCE) with Pumas - Andreas Noack (presenter), Patrick Kofod Mogensen, Joakim Nyberg, Vijay Ivaturi

• Subspace MCMC Algorithm for Bayesian Parameter Estimation of Hierarchical PK/PD models in Pumas - Manu Francis (presenter), Vijay Ivaturi, Mohamed Tarek

• Latexify.jl - An Ecosystem for Automatically Transcribing Pumas and Julia Objects into Renderable Equations. - Niklas Korsbo (presenter)

Julia Computing and Pumas-AI at American Conference on Pharmacometrics (ACoP): Julia Computing and Pumas-AI are teaming up at the American Conference on Pharmacometrics (ACoP) November 8-12. Pumas-AI CEO Joga Gobburu will present An Integrated Framework Leveraging Mechanistic and AI Approaches to Gain Deeper Insights into Target Engagement.

COVID-19 Dashboard Using Julia: Julia Computing Director of Applications Engineering Matt Bauman published a COVID-19 dashboard using Julia to display county-level data from the New York Times. Dashboards such as these can be built by users of JuliaHub. JuliaHub makes it easy to gather data from various sources and create dashboards for analysis and visualization. Julia’s performance makes it easy for dashboards such as these to start out with small datasets and handle them as they grow without the need for rewriting the underlying analytics. This particular dashboard was implemented when the dataset was just 37 thousand rows, and continues to work effortlessly with 1.73 million rows today. Click here to explore.

Careers at Julia Computing: Julia Computing is a fast-growing tech company with fully remote employees in 10 countries on 4 continents. Click the links below to learn more about exciting careers with Julia Computing.

• Compiler and Runtime Engineer

• Cloud Engineer

• Sales Engineer

• Julia Application Developer

• Security Engineer

Julia Computing is also looking to fill internship positions:

• GUI and 3D Visualization Intern

• Semiconductor EDA Intern

Please click here for more information and to apply.

Free Webinars from Julia Computing and Pumas-AI: Register today to participate in a free one hour Webinar from Julia Computing or Pumas-AI.

The Two-Language Problem, Software 2.0 and a Supercomputer at Your Fingertips: Julia Computing CEO and co-founder and Julia co-creator Viral Shah spoke with Shripati Acharya as part of a Prime Venture Partners podcast. Click here to listen to the podcast or here to read the article in YourStory.

“Simulations Are About to Get Way, Way Faster with JuliaSim”: Not a Monad Tutorial interviewed Dr. Chris Rackauckas (Julia Computing) about JuliaSim, “a cloud-based simulation platform [from Julia Computing] built on top of the Julia open source stack, including SciML and ModelingToolkit.” More information is available here.

HackerRank CEO Vivek Ravisankar Says Julia’s Dynamism and Speed Makes Julia Better for High-Performance Machine Learning: “I don’t see Java evolving toward more data science use. JVM languages, like Kotlin and Scala, are more suitable for data science workloads, but it’s unlikely they will replace Python, simply due to the learning curve,” says Vivek Ravisankar, CEO and co-founder of HackerRank. Ravisankar believes that a language like Julia has a better chance at being used for high-performance ML workloads, given its dynamism and speed.

SciML Receives Chan Zuckerberg Initiative Funding: SciML has been awarded funding from the Chan Zuckerberg Initiative for research on spatial stochastic simulation algorithms, identifiability and compile times. More information is available here.

New Julia Books: Two new Julia books have been released: Numerical Linear Algebra with Julia by Eric Darve and Mary Wootters and Statistics with Julia by Yoni Nazarathy and Hayden Klok.

Julia for Astronomy: Astronomers and astrophysicists from the University of Washington, Institut d’Astrophysique de Paris, the Harvard-Smithsonian Center for Astrophysics and the MIT Kavli Institute for Astrophysics and Space Research use Julia to model transit times, radial velocity and astrometric positions. Their paper, A Differentiable N-Body Code for Transit Timing and Dynamical Modeling, has been published by the Royal Astronomical Society.

Julia for Planning School Bus and School Opening Schedules: San Francisco’s public schools have joined Boston and other public school systems around the country using JuMP to optimize school bus and school opening schedules with Julia.

“San Francisco Unified School District (SFUSD) partnered with a research team to write an algorithm that can sift through every potential scenario to help SFUSD staff make better decisions. The team consists of three researchers: Sebastien Martin, a professor at Northwestern’s Kellogg School of Management; Arthur Delarue, a fifth year PhD student at MIT; and Zhen Lian, a fifth year PhD student at Cornell. All three have experience working on similar transportation optimization problems for places like Boston Public Schools, Denver Public Schools, and Lyft. The algorithm took input from SFUSD staff, combed through all of the potential solutions, and showed thousands of possible scenarios that met the District’s goals:

• Reduce transportation costs

• Shift older students into later start times

• Add a weekly early release day to support planning and professional development for teachers

• Minimize changes for families, especially at the elementary school level”

More information is available here.

Techblitz: Techblitz has published a new feature on Julia Computing. Techblitz says, “Easy to use as Python and as fast as C, Julia Computing supports a programming language with outstanding applicability.”

Spectrum Instrumentation Pioneers “Julia” SDK for High Performance Applications: Spectrum Instrumentation has created a software development kit (SDK) for programming its full range of over 200 different digitizers, generators and digital I/O products using Julia. According to Microwave Journal, “Combining Julia with Spectrum Instrumentation products also helps to speed up processing and reduce latency. The Spectrum products offer ultrafast data transfers with a variety of different acquisition and generation modes (such as single, multiple, gated and FIFO) which helps to optimize testing throughputs. It is a key benefit for situations that require fast decision making and it is one of the reasons why Spectrum products can be found working in applications involving autonomous vehicles, robotics, drones, imaging devices, medical appliances and control systems.”

Technical.ly Features Pumas-AI: Pumas-AI Looks to Democratize Access to Drug Development Tools explains how Pumas-AI and Julia Computing partnered to create the world’s fastest, most powerful and most efficient end-to-end platform for pharmaceutical modeling and simulation. “[Pumas-AI] developed its software through a partnership with Julia Computing, an MIT spinout which seeks to bring products to the world that use the Julia programming language and recently raised $24 million. It effectively makes the startup an entry point for healthcare into using Julia.”

The Julia Journal from Julia Frank: Physics researcher Julia Frank has launched a new blog about Julia. Recent blog entries include:

• My Preferred Julia IDE After 3 Months of Coding

• Embedding Jupyter and Pluto Notebooks in a Blog Post Is Easy

• Creating Custom Plot Markers in JuliaLang in 2 Ways

• Gnuplot with Julia for Powerful Plotting Option

• Learning Parallel Computing in Julia as an Absolute Beginner

• Number One JuliaLang Beginner Tip: Do Not Delay the Active Learning

• JuliaLang on M1 Chip – It Works! New Install and Get Started as an Absolute Beginner

• Artificial Spin Ice. A New Exciting Topic in Nanomagnetism Research

• Artificial Spin Ice Research Motivated this Blog

Fastnet.jl - Linear Time Discrete Event Network Simulator: Thilo Gross has introduced Fastnet.jl, a Julia package that allows very fast (linear-time) simulation of discrete-state dynamical processes on networks, such as commonly studied models of epidemics. More information is available here.

Why You Should Make Non-Technical Open-Source Software Contributions - Logan Kilpatrick: Julia community manager Logan Kilpatrick presented Why You Should Make Non-Technical Open-Source Software Contributions to the GSOC Student Summit. Click here to watch.

Makie.jl - Flexible High-Performance Data Visualization for Julia: Simon Danisch and Julius Krumbiegel published Makie.jl - Flexible High-Performance Data Visualization for Julia in the Journal of Open Source Software. Click here for the article.

Julia Data Science: Jose Storopoli, Rik Huijzer and Lazaro Alonso published a free and open source book on data science using Julia. Click here to read or download.

Pumas for Academic Use: Pumas-AI and Julia Computing have made the Pumas pharmaceutical modeling and simulation platform available to academic users. Click here for more information about Pumas and academic use.

Julia in Visual Studio Code: Julia in Visual Studio Code is now available in the official Visual Studio Code documents. Click here for more information.

Pumas - Enhanced and In the Cloud: Pumas-AI has launched an enhanced version of Pumas that is readily accessible in the cloud. Pumas is the revolutionary advanced healthcare analytics platform that facilitates quantitative capabilities across the drug development cycle. Designed from the ground up in Julia, Pumas allows users to scale, integrate and accelerate their quantitative scientific activities all under one umbrella. Pumas is a product of Pumas-AI and deployed through the JuliaHub platform from Julia Computing to leverage JuliaHub's ease of use and scalability. Julia Computing is a technology partner and exclusive reseller of Pumas. Click here for more information.

JuliaHub from Julia Computing: 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.

More information is available in these two presentations from Dr. Matt Bauman (Julia Computing):

• JuliaHub - The Best Way to Run Large Scale Computing in the Cloud

• GUI Front Ends for Julia Programs on JuliaHub

JuliaSim: JuliaSim is a next generation cloud-based modeling and simulation platform, combining the latest techniques from scientific machine learning with equation-based digital twin modeling and simulation. There is more information about JuliaSim available in this presentation from Dr. Chris Rackauckas.

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.

Julia and Julia Computing in the News

• SDTimes: Julia Computing Receives DARPA Grant

• Inside Big Data: Above the Trend Line

• BBN Times: Are the Highly Marketed Deep Learning and Machine Learning Processors Just Simple Matrix Multiplication Accelerators?

• I Programmer: Jupyter Project Celebrates 20 Year Anniversary

• Market Research Telecast: Survey - Performance Is the Most Important Argument in Favor of the Julia Programming Language

• Heise: Umfrage: Performance Ist das Wichtigste Argument für Programmiersprache Julia

• The Server Side: Java Shows Promise for Scaling AI Apps

• Microwave Journal: Spectrum Instrumentation Pioneers “Julia” SDK for High Performance Applications

• Not A Monad Tutorial: Simulations Are About to Get Way, Way Faster with JuliaSim

• YourStory: Entrepreneur Viral Shah Talks About Starting Up in the Open-Source Computing Industry

• FinanzNachrichten: Julia Computing Nimmt 24 Mio. USD in Serie-A-Finanzierung Ein, Ehemaliger Snowflake-CEO Bob Muglia Tritt Dem Vorstand Bei

• Techblitz: Easy and Fast Machine Learning with Julia, the Latest Programming Language from MIT

• Technical.ly: Pumas-AI Looks to Democratize Access to Drug Development Tools

• Baltimore Fishbowl: Pumas-AI Looks to Democratize Access to Drug Development Tools

• PhysicsWorld: Standing on the Shoulders of Programmers: the Power of Free and Open-Source Software

• Florida News Times: JetBrains Previews Data Science IDE

• Analytics Insight: Top 15 Tools Every Data Scientist Should Bring to Work

• Reseller News: JetBrains Previews Data Science IDE

Julia Blog Posts

• My Preferred Julia IDE After 3 Months of Coding (Julia Frank)

• Embedding Jupyter and Pluto Notebooks in a Blog Post Is Easy (Julia Frank)

• Creating Custom Plot Markers in JuliaLang in 2 Ways (Julia Frank)

• Gnuplot with Julia for Powerful Plotting Option (Julia Frank)

• Learning Parallel Computing in Julia as an Absolute Beginner (Julia Frank)

• Number One JuliaLang Beginner Tip: Do Not Delay the Active Learning (Julia Frank)

• JuliaLang on M1 Chip – It Works! New Install and Get Started as an Absolute Beginner (Julia Frank)

• Artificial Spin Ice. A New Exciting Topic in Nanomagnetism Research (Julia Frank)

• Artificial Spin Ice Research Motivated this Blog (Julia Frank)

• Handling Vectors of Vectors in DataFrames.jl (Bogumił Kamiński)

• ABC of Handling Missing Values in Julia (Bogumił Kamiński)

• SciML Common Interface Expansion

• SciML Receives Chan Zuckerberg Institute Funding: Spatial SSAs, Identifiability, and Compile Times

• Diffusion on Graphs (Cory SImon)

• Wrapping Up GSOC (Balaje Kalyanaraman)

• Transfer Learning with Flux (Dhairya Gandhi)

• Fixed Width Strings in CSV.jl (Bogumił Kamiński)

• Javis.jl Examples Series: Inverse Kinematics (Ole Kröger)

Upcoming Julia Events

• Virtual Conference: Modelica Conference with Julia Computing Sep 20-24

• Virtual Conference: Geostats.jl Workshop with Júlio Hoffimann at FOSS4G Sep 28

• Webinar: Financial Modeling on Large, Streaming Datasets with Dr. Josh Day (Julia Computing) Oct 14

• Webinar: Efficient Use of 505 b(2) Pathway to Enter US Market with Dr. Joga Gobburu (Pumas-AI) Oct 21

• Virtual Conference: American Conference on Pharmacometrics with Julia Computing and Pumas-AI Nov 8-12

Recent Julia Online Events

• Webinar: GPU Programming in Julia with Dr. Tim Besard (Julia Computing) Aug 26

• Virtual Conference: Population Approach Group Europe (PAGE) with Julia Computing Sep 2-7

• Virtual Meetup: A Client Interface in Julia - TypeDBClient.jl with Mark Schulze, Frank Urbach, Daniel Crowe and TypeDB New York Engineers Sep 7

• Virtual Meetup: CUDA.jl with Boulder Data Science, Machine Learning and AI Sep 9

• Virtual Meetup: Poisson Regression and Introduction to Julia with Steve Simon and Kansas City R Users Group Sep 11

Contact Us: Please contact us if you wish to:

• Purchase or obtain license information for products such as JuliaHub, JuliaSim or Pumas

• Obtain pricing for Julia consulting projects for your organization

• Schedule online Julia training for your organization

• Share information about exciting new Julia case studies or use cases

• Spread the word about an upcoming online event involving Julia

• Partner with Julia Computing to organize a Julia event online

• Submit a Julia internship, fellowship or job posting

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 JuliaSPICE 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.