Newsletter – May 2018

We wanted to thank all Julia users and well wishers for the support and for being part of the Julia Community, and to give an update on some exciting developments for May 2018:

  1. Julia in the News
  2. Julia Training Videos
  3. JuliaCon 2018
  4. Recent Julia Blog Posts
  5. Upcoming and Recent Julia Events and Meetup Groups
  6. Julia Jobs, Fellowships and Internships
  7. Contact Us, About Julia and Julia Computing

1. Julia in the News

Increment: Julia – The Goldilocks Language

“The initial drive behind Julia was the desire for a programming language that combined elements of the high-level functionality of MATLAB and R with the speed of C or Ruby – as [Julia co-creator Stefan] Karpinski put it, ‘the best of all worlds.’ …they wanted ‘a Goldilocks programming language – one that was high level and low level at the same time, depending on how you used it,’ said Karpinski. The Goldilocks ideal gave way to the Julia project: an open-source, dynamic programming language…”

TensorFlow: Why Swift for TensorFlow?

“Julia is another great language with an open and active community. They are currently investing in machine learning techniques, and even have good interoperability with Python APIs. The Julia community shares many common values as with our project, which they published in a very like-minded blog post after our project was well underway. We are not experts in Julia, but since its compilation approach is based on type specialization, it may have enough of a representation and infrastructure to host the Graph Program Extraction techniques we rely on.”

Inside Venture Capital: Interview with Julia Computing’s Viral Shah (Note: Subscription Required)

“The inspiration for Julia Computing is the demand for Julia,” says Viral Shah.

The Atlantic: The Scientific Paper Is Obsolete. Here’s What’s Next.

“The Jupyter notebook, as it’s called, is like a Mathematica notebook but for any programming language. You can have a Python notebook, or a C notebook, or an R notebook, or Ruby, or Javascript, or Julia.”

CIO: The Thin Line Between High Performance Computing and AI

“Rajshekar Behar, marketing leader at Julia Computing – a rapidly rising startup specializing in AI solutions – says: ‘I believe we keep mixing HPC with AI. AI is an application that needs high performance computing. When you start solving a problem, you reach a point where you want to delegate the decision making to a system, because you think it’s going to take better decisions. And that’s when you implement AI with HPC,’ he explains.”

TheNextPlatform: Will Chapel Mark Next Great Awakening for Parallel Programmers?

“HPC is still committed to its lower level tools and that will remain the case with domain scientists dabbling in Python until it fails to scale. This seems to clear the way for either Julia or Chapel.”

YourStory: India’s Tryst with Data and AI – How Its Tech Is Going Global

“‘I do think that, at a business level, as well as at a national level, we have a lot of catching up to do…’ says Viral Shah, co-founder of Julia Computing. He adds that Indian firms today have a lot of data but the skill in asking questions on what can be done with data for effectively building AI models is missing.”

CIO: Why Industry and Academia Believe Data Liquidity Is Key to AI Growth in India

“Rajshekar Behar, marketing leader at Julia Computing, a rapidly rising startup specializing in AI solutions, believes that investing in learning holds the key to AI growth. ‘The major barrier I see is that the gap between haves and have-nots keeps increasing. One way to tackle this problem is to invest in the infrastructure of learning. The skillset required for AI is not rocket science, so colleges need to include AI in their curriculum,’ he adds.”

InfoWorld: Ruby (Finally) Gains In Popularity But Go Plateaus

“Also in this month’s index, Kotlin and Julia both entered the top 40… Julia, in 37th place … is used in scientific computing and [the] burgeoning field of machine learning.”

Analytics India: Top 5 Alternatives to Jupyter Notebook

“The RStudio community plans to provide support to other languages such as Julia and Haskell.”

Datanami: Apache Zeppelin Launches Latest Data Science Notebook

“Apache Zeppelin joins a growing list of data science notebooks that include Databricks Cloud, Jupyter (the successor to the iPython Notebook), R Markdown, Spark Notebook and others. Backends to multiple languages include Python, Julia, Scala, SQL and others.”

2. Julia Training Videos

We are introducing several new online, instructor-led, and video training options for Julia. For more information on online and instructor-led classes, please contact training@juliacomputing.com. Training videos are also available online:

3. JuliaCon 2018:

JuliaCon is coming to University College London Aug 7-11, 2018.

4. Recent Julia Blog Posts

5. Upcoming and Recent Julia Events and Meetup Groups

a. Upcoming Julia Events

b. Recent Julia Events

c. Julia Meetup Groups:

There are 26 Julia Meetup groups worldwide with more than 5 thousand members. If there’s a Julia Meetup group in your area, we hope you will consider joining, participating and helping to organize events. If there isn’t, we hope you will consider starting one.

6. Julia Jobs and Internships

Do you work at or know of an institution looking to hire Julia programmers as staff, research fellows or interns? Would your employer be interested in hiring interns to work on open source packages that are useful to their business? Help us connect members of our community to great opportunities by sending us an email, and we’ll get the word out!

There are more than 200 Julia jobs currently listed on Indeed.com, including jobs at Google, Facebook, IBM, KPMG, Ernst & Young, Booz Allen Hamilton, Comcast, Zulily, National Renewable Energy Research Laboratory, Los Alamos National Laboratory, Brown, Princeton, Columbia, Notre Dame, MIT, University of Chicago and many more.

7. Contact Us

Please contact us if you wish to:

  • Purchase or obtain license information for Julia products such as JuliaPro, JuliaPro Enterprise, JuliaRun, JuliaDB, JuliaFin or JuliaBox
  • Obtain pricing for Julia consulting projects for your organization
  • Schedule Julia training for your organization
  • Share information about exciting new Julia case studies or use cases
  • Spread the word about an upcoming conference, workshop, training, hackathon, meetup, talk or presentation involving Julia
  • Partner with Julia Computing to organize a Julia meetup, conference, workshop, training, hackathon, talk or presentation involving Julia
  • Submit a Julia internship or job posting

About Julia and Julia Computing

Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and many other domains. 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. For example, Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, the world’s sixth-largest supercomputer. With more than 1.8 million downloads and +101% annual growth, Julia is one of the top programming languages developed on GitHub. Julia adoption is growing rapidly in finance, insurance, machine learning, energy, robotics, genomics, aerospace, medicine and many other fields.

Julia Computing was founded in 2015 by all the creators of Julia to develop products and provide professional services to businesses and researchers using Julia. Julia Computing offers the following products:

  • JuliaPro for data science professionals and researchers to install and run Julia with more than one hundred carefully curated popular Julia packages on a laptop or desktop computer.
  • JuliaRun for deploying Julia at scale on dozens, hundreds or thousands of nodes in the public or private cloud, including AWS and Microsoft Azure.
  • JuliaFin for financial modeling, algorithmic trading and risk analysis including Bloomberg and Excel integration, Miletus for designing and executing trading strategies and advanced time-series analytics.
  • JuliaDB for in-database in-memory analytics and advanced time-series analysis.
  • JuliaBox for students or new Julia users to experience Julia in a Jupyter notebook right from a Web browser with no download or installation required.

To learn more about how Julia users deploy these products to solve problems using Julia, please visit the Case Studies section on the Julia Computing Website.

Julia users, partners and employers hiring Julia programmers in 2018 include Amazon, Apple, BlackRock, Booz Allen Hamilton, Capital One, Comcast, Disney, Ernst & Young, Facebook, Ford, Google, IBM, Intel, KPMG, Microsoft, NASA, Oracle, PwC, Uber, and many more.