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Newsletter November 2021 - Julia’s SciML Takes Center Stage at Argonne National Laboratory AI for Science Colloquium

12 November 2021 | Julia Computing

Julia’s SciML Takes Center Stage at Argonne National Laboratory AI for Science Colloquium: “Keynote speaker Jonathan Rowe (University of Birmingham, Alan Turing Institute) noted there is a fair amount of work ... being done around benchmarking and cited work by the SciML group. “I want to call out one because this is done by the guys from the scientific machine learning group at the Rutherford [Appleton] labs, who we collaborate with. They’ve started putting forward something called SciML bench available on GitHub, which is [a] really good start [to] putting together a framework to do this. And they’ve got data now from environmental sciences, particle physics, astronomy, and so forth, where you can begin to do this benchmarking, if you’re interested in that. Please go and check it out and see how we might be able to add to it and help,” he said.” The article notes that “the majority of the tooling for SciML is built using the Julia programming language.”

JuMP Creators Awarded Beale - Orchard-Hayes Prize: Iain Dunning, Joey Huchette and Miles Lubin have been awarded the Beale - Orchard-Hayes Prize for the creation of JuMP, the primary mathematical optimization package in Julia. Miles Lubin, Senior Research Scientist at Google Research (Algorithms and Optimization), published JuMP: A Modeling Language for Mathematical Optimization which explains some of the highlights.

Betterment Uses Julia for Financial Advising: Betterment is a financial advisory company that helps clients manage $29 billion in assets. Dan Felicetta explains Why (And How) Betterment Is Using Julia: “[W]e’ve invested significant resources in modernizing ... by converting our codebase from R to Julia and we’re now able to ship updates to our quantitative models quicker, and with less risk of errors being introduced in translation. Currently, Julia powers all the projections shown inside our app, as well as a lot of the advice we provide to our customers. The Julia library we built for this purpose serves around 18 million requests per day, and very efficiently at that.”

Genie v4.0.0 Released: Genie.jl, the highly productive Julia web framework, is now available with a new v4.0.0 release. Click here for more information.

Pumas-AI, Julia Computing and Roche Scientists Receive American Conference on Pharmacometrics (ACoP) Mathematical and Computational Sciences Special Interest Group (MCS SIG) Poster Award: The American Conference on Pharmacometrics (ACoP) Mathematical and Computational Sciences Special Interest Group (MCS SIG) Poster Award was awarded to Niklas Korsbo (Pumas-AI, University of Cambridge), Chris Elrod (Julia Computing), Francesco Brizzi (Roche), Antoine Soubret (Roche), Andreas Noack (Julia Computing), Vijay Ivaturi (Pumas-AI, University of Maryland School of Pharmacy) and Christopher Rackauckas (Julia Computing, MIT) for Automatic Identification of Non-Obvious Prognostic Factors in Big Data with DeepNLME in Pumas. More information is available here.

Former FDA Pharmacometrics Director Dr. Yaning Wang Joins Pumas-AI Scientific Advisory Board: Pumas-AI announced that Dr. Yaning Wang, former FDA Pharmacometrics Director, has joined the Pumas-AI Scientific Advisory Board.

Julia Data Science: Julia Data Science by Jose Storopoli, Rik Huijzer and Lazaro Alonso is now available for purchase from Amazon. Click here to purchase.

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

Webinar Presenter(s) Length of Webinar Date Time Registration Link Cost
TXA PKPD in Pregnant Women Ms. Shuhui Li, University of Maryland, Baltimore and Dr. Homa Ahmedazia, MD, George Washington University Hospital 1 hour Mon Nov 15 9:30 - 10:30 am Eastern (US) Register Free
Performance Benchmarking in Julia Jameson Nash, Julia Computing 1 hour Tue Nov 30 12 noon - 1 pm Eastern (US) Register Free

Beacon Biosignals Uses Julia for Precision Medicine and Announces $27 Million Series A: Beacon Biosignals uses Julia to accelerate clinical trials and enable new treatments for patients with neurological and psychiatric disease. Beacon Biosignals has announced $27 million Series A financing to scale its EEG neurobiomarker discovery platform.

Julia for Epidemiology: Julia use continues to grow in epidemiology and other areas of statistics and bioscience. Dr. Tomás Aragón, Director of the California Department of Public Health, California State Public Health Officer and Professor of Epidemiology at the UC Berkeley School of Public Health explains why he switched from R to Julia: “[Julia] is for scientific computing, easy to learn, and as fast a[s] C++. Can run R or Python from Julia. Visit [the] future at!”

New Flux Tutorials - Generative Adversarial Networks: Two new Flux tutorials are now available:

Introduction to Probability for Data Science: Julia joins MATLAB and Python as the third language included in Stanley Chan’s Introduction to Probability for Data Science. The book is available here.

Free Online Course from MIT - Introduction to Computational Thinking - New Website: The blockbuster free online course from MIT - Introduction to Computational Thinking - is taught using Julia and course materials are available for free online at a new Website. Course instructors are Alan Edelman, David Sanders and Charles Leiserson.

MathOptInterface: MathOptInterface - A Data Structure for Mathematical Optimization Problems by Benoît Legat, Oscar Dowson, Joaquim Dias Garcia and Miles Lubin has been published by Informs Journal on Computing. According to PSR, “MathOptInterface is the backend of the JuMP library for optimization in Julia, one of the most powerful systems for solving large-scale stochastic optimization problems. It is an open-source package specially designed to make JuMP optimization routines even more flexible and facilitate the development of advanced optimization algorithms central to several areas such as planning and operation of the electricity system worldwide.”

Working with Flux.jl Models on the Hugging Face Hub ?: Logan Kilpatrick has published a new article - Working with Flux.jl Models on the Hugging Face Hub ?. Click here for more information.

Building Deep Learning Models in Flux.jl: Logan Kilpatrick recorded a new video on Building Deep Learning Models in Flux.jl. Click here to view.

Julia v1.7 Release Coming Soon: Julia v1.7 release candidate is undergoing testing now. New Features Coming in Julia 1.7 by Lee Phillips is available on

New Julia for Energy Case Studies: Électricité de France (EDF) and Los Alamos National Laboratory are featured in two new case studies at Nearly 50 different companies and organizations are featured in case studies at, including case studies in finance, energy, pharmaceuticals, geology, cryptography, sports, robotics, medicine, biology, genomics, astronomy, manufacturing, logistics, transportation and operations research. If you are part of or aware of interesting Julia applications or use cases that would make for a good case study, please contact

35 Julia Computing and Pumas-AI Webinars Are Available Online: 35 Julia Computing and Pumas-AI Webinars from 2020 and 2021 are available for free online at Most Webinars are one hour long and include topics such as financial modeling, GPU programming, machine learning, building production applications, parallel computing, quantitative systems pharmacology and much more - all in Julia. If you have a Webinar topic to suggest, please email your suggestion to

Composability in Julia: Qiyao Wei, Frank Schäfer, Avik Pal and Chris Rackauckas have published Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs. This blog post explains “how to easily, efficiently, and robustly use steady state nonlinear solvers with neural networks in Julia, ... the relationship between steady states and ODEs, thus connecting the methods for Deep Equilibrium Models (DEQs) and Neural ODEs [and] ... how DiffEqFlux.jl can be used as a package for DEQs, showing how the composability of the Julia ecosystem naturally lends itself to extensions and generalizations of methods in machine learning literature.”

Introduction to Julia: Anshul Tayal has published a new Introduction to Julia, including benchmarks and instructions for installation, how to run Julia in a Jupyter notebook, package installation and more. Click here for more information.

Using JSON Web APIs from Julia: Josh Day has published a blog post about Using JSON Web APIs from Julia at Julia for Data Science. Click here for more information.

Julia Computing at a (Virtual) Conference Near You: Julia Computing participates in 30-50 conferences every year - that’s approximately 1-2 conferences per week. Recent and upcoming conferences and conference presentations include:

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 and internships with Julia Computing.

Please click here for more information and to apply.

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):

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

  • iThome: [Julia臺灣社群發起人親自解答]<wbr>12個問答快速認識Julia

  • BuiltIn: 11 Data Science Programming Languages to Know

  • MarkTechPost: JuMP: An AML Based Modeling Language Embedded In Julia For Mathematical Optimization

  • Analytics Insight: Difference Between Coding in Data Science and Machine Learning

  • Youth Incorporated: These 6 Skills Are A Must To Master For A Data Science Career

  • Nature Communications: From Calibration to Parameter Learning: Harnessing the Scaling Effects of Big Data in Geoscientific Modeling

  • Nature Communications: Underwater CAM Photosynthesis Elucidated by Isoetes Genome

  • TechBeacon: 8+ Programming Languages that Will Keep You in Demand

  • Analytics India: JupyterLab Desktop App vs JetBrains DataSpell

  • Inixindo Jogja: Berkenalan Dengan Julia, Bahasa Pemrograman Berperforma Tinggi Tapi Mudah Dipelajari

  • Glints: Penting Untuk Data Science, Yuk, Kenalan Dengan Bahasa Pemrograman Julia

  • Warta Ekonomi: Apa Itu Bahasa Pemrograman Julia?

  • Analytics Insight: Top 10 C++ Frameworks for Machine Learning in 2021

  • Boston Globe: Amid a Buzzy Tech Scene, Kendall Square Stalwart Akamai Has to Reinvent Itself

  • Analytics Insight: 10 Good to Know Programming Languages for Data Scientists

  • Analytics Insight: Top Machine Learning as a Service Providers to Know in 2021

  • HPCWire: Argonne AI for Science Colloquium Marks Challenges and Progress

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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.

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