Cambridge, MA – Julia Computing has been awarded funding by the US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) to “develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation and air conditioning (HVAC) systems in buildings.”

Funding was awarded as part of the Design Intelligence Fostering Formidable Energy Reduction and Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program.

According to the US Department of Energy, “As of 2012, buildings consume 40 percent of the nation’s primary energy, with HVAC systems comprising a significant portion of this consumption. It has been demonstrated that the use of modeling and simulation tools in the design of a building can yield significant energy savings—up to 27 percent of total energy consumption. However, these simulation tools are still too slow to be practically useful. Julia Computing seeks to improve upon these tools using the latest computing and mathematical technologies in differentiable programming and composable software to enhance the ability of engineers to design more energy efficient buildings.”

Julia Computing CEO Viral Shah further explains, “Differentiable programming represents a broad generalization of AI technologies. This capability of combining scientific insights with learning from data will make it possible for Julia to improve the design processes that lead to energy savings across the board.”

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 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 more than 11 million times 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.

Julia Computing was founded in 2015 by all the creators of Julia to provide products including JuliaTeam, JuliaSure and JuliaRun to businesses and researchers using Julia.

NVIDIA announced that Julia is “now available through containers for x86 and Arm” and “can be used for GPU programming by writing CUDA kernels in Julia or by using the powerful array programming model.” For more information, please read “Julia Computing and NVIDIA Bring GPU Computing to Arm.”

US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) has awarded funds to Julia Computing to “develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation and air conditioning (HVAC) systems in buildings.” Funding was awarded as part of the Design Intelligence Fostering Formidable Energy Reduction and Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program.

Julia Computing’s Alan Edelman Receives Sidney Fernbach Award at SC19 from Institute of Electrical and Electronics Engineers (IEEE): Alan Edelman was awarded the Sidney Fernbach Award for “outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language.” Alan is co-creator of Julia, co-founder and Chief Scientist at Julia Computing, director of the Julia Lab at MIT and Professor of Applied Mathematics at MIT. Alan accepted the award at SC19 in Denver, Colorado.

Julia Computing Enterprise Solutions: Contact Julia Computing for more information about putting Julia to work for your organization, deploying Julia more efficiently, effectively and at scale.

• JuliaSure:: JuliaSure provides enterprise support and indemnity for organizations: using Julia.

• JuliaTeam: JuliaTeam provides enterprise governance including private and package development, deployment, management, security, support and indemnity.

• JuliaRun: JuliaRun allows you to scale Julia deployment from a single machine to dozens or hundreds of nodes in a public or private cloud environment, including AWS, Azure or Google Cloud.

Julia Computing Webinar on Private Package Management and Governance with Julia: Please register to participate in a Julia Computing Webinar on Private Package Management and Governance with Julia. The Webinar is led by Julia Computing Senior Research Scientist Matt Bauman and takes place on Wed Dec 4 from 12-1 pm EST.

JuliaBox 30 Day Free Trial: JuliaBox is now available with a 30 day free trial. JuliaBox is the fastest and easiest way to start using Julia right away with no download required. Register today to start your 30 day free trial.

JuliaBox Academic Discount: Hundreds of students and faculty at universities around the world use JuliaBox for classroom instruction and learning. Use free and open source materials to design your own course using Julia. JuliaBox starts at just $7 per month including a 50% academic discount. Sign up online or contact Julia Computing to take advantage of the academic discount or for more information.

Julia 1.3.0: Julia 1.3.0 has been released with a number of new features, including new multithreading capabilities. The release was featured on Hacker News. Click here to download and review release notes.

Machine Learning on Encrypted Data Without Decrypting It: Julia Computing co-founder and CTO (Tools) Keno Fischer published a blog post explaining how Julia can be used for machine learning using encrypted data without decryption. The blog post was featured on Hacker News.

Reliable and Reproducible Binary Artifacts for Julia Packages: Julia Computing’s Elliot Saba, co-founder and CTO (Open Source) Stefan Karpinski and Kristoffer Carlsson published a blog post about changes to the Julia package manager that make it easier to produce reliable and reproducible binary artifacts for Julia packages. This new feature is available as part of the Julia package manager for Julia v1.3.0 and subsequent versions.

AWS Provides Promotional Credits for Julia: Amazon Web Services (AWS) is providing promotional credits for Julia and other open source projects. Visit AWS Open Source for more information.

Julia Joins Google Code-In Contest: Julia has been selected by Google to participate in the Google Code-In program which introduces teens aged 13-17 around the world to open source development. If you know students, parents or teachers who may be interested, please direct them to Google Code-In for more information. Please read the blog post by Julia Computing VP Engineering Avik Sengupta and Logan Kilpatrick.

Julia Day in New York Video Now Available Online: Julia Computing’s Julia Day in New York took place a few weeks ago, and the videos are now available online.

1. The Story of Julia with Julia Co-Creators and Julia Computing Co-Founders Viral Shah and Stefan Karpinski

2. Julia Computing Products, Services and Training with Julia Computing VP Sales Jon Shepherd

3. State Street Case Study with State Street’s Elton Pereira

4. Conning Case Study with Conning Managing Director David Weiss

Tanmay Teaches Julia for Beginners: Tanmay Bakshi’s Tanmay Teaches Julia for Beginners is now available on Amazon. Tanmay Bakshi is a 15 year old author, AI/ML expert, TED speaker, IBM Champion for Cloud and Google Developer Expert for Machine Learning. His ‘Tanmay Teaches’ YouTube channel has 323 thousand subscribers.

New Julia Benchmarks vs. Python, Matlab and R: Chris Rackauckas published new reproducible benchmarks demonstrating Julia’s superior performance for ordinary differential equations (ODEs) compared with Python, Matlab and R.

Julia’s GPU Performance Benchmarked: Grant McDermott (University of Oregon) reports that Julia’s FixedEffectModels package with GPUs is ‘at least 2x faster than the next-best option’.

International Energy Agency (IEA) Benchmarks Julia: The International Energy Agency published Julia benchmarks demonstrating that Julia’s JuMP package is 400% faster than GNU Mathprog and 680% faster than Python’s Pyomo for energy modeling with the Integrated MARKAL-EFOM System (TIMES).

Julia Ranks #1 in 30, 60, 90 and 120 Day Growth on ModuleCounts.com: Julia ranked first in 30, 60, 90 and 120 day growth on ModuleCounts.com.

Julia for Medicine: Robert Gregg and Jason Shoemaker presented their use of Julia for agent-based modeling of the CGAS pathway at the American Institute for Chemical Engineers annual conference. Gregg and Shoemaker are researchers at the University of Pittsburgh Shoemaker Immunosystems Lab and this technique is used for detecting pathogenic DNA. The paper they presented is Quantifying the Impact of Cellular Heterogeneity on CGAS Pathway Regulation Using Multiscale Agent-Based Modeling.

JuliaNantes Video and Presentations: Presentations and video from the June 17, 2019 Journée Julia et Optimisation at l’Université de Nantes are now available online. Click here to view.

JuliaCon 2020: JuliaCon 2020 will take place July 27-31 at ISCTE – Instituto Universitário de Lisboa (ISCTE-IUL) in Lisbon, Portugal. Stay tuned for more details.

Julia and Julia Computing in the News

• PacktHub: Julia Computing Research Team Runs Machine Learning Model on Encrypted Data Without Decrypting It

• InsideBigData: Julia – The Programming Language Of The Future

• I-Programmer: Julia Improves Multithreading

• JAXenter: Julia v1.3: Reproducible Results, Yggdrasil, & Multi-Threading Changes

• Analytics India: Is Julia Finally Catching Up with Python & R?

• DevClass: Julia 1.3 Offers Experimental Approach to Multi-Threading

• HPCWire: Alan Edelman Wins Sidney Fernbach Award

• ExecutiveBiz: Galois to Develop Secure Computing Tech Under IARPA Program

• HostReview: Which Machine Learning Frameworks To Try In 2019-20

• Nature: Make Code Accessible with these Cloud Services

• InsideHPC: Deep Learning on Summit Supercomputer Powers Insights for Nuclear Waste Remediation

• ScienceBlog: Deep Learning Expands Study Of Nuclear Waste Remediation

• Finextra: The Non-Contradiction of Proprietary Finance and Community Open Source Programming

• Towards Data Science: Artificial Intelligence & Deep Learning for Medical Diagnosis

• BioSpace: MMS Holdings Doubles Growth in South Africa Region Amid Burgeoning Pharmaceutical Industry Locally

• EurekAlert: Deep Learning Expands Study of Nuclear Waste Remediation

• PacktHub: Julia v1.3 Released with New Multithreading Features, and Much More

• ZDNet: Programming Languages – Python Overtakes Java on GitHub as Google Dart Use Soars

• SD Times: News Digest – Amazon Announces Rekognition Custom Labs, Scala.js 1.0.0-RC1, and Julia 1.3

• Analytics India: Python Just Overtook Java on GitHub, But How Did It Fare Overall?

• CMU: CMU & Partners Receive ARPA-E Award for Machine Learning-Accelerated Discovery of Energy Materials

• Tech India Today: 10 Best Programming Languages For Artificial Intelligence (AI) in 2020

• Analytics India: Julia Computing Uses Homomorphic Encryption for ML. Is It the Way Forward?

Julia Blog Posts

• How To Turn Physics into an Optimization Problem? (Mark Saroufim)

• Machine Learning on Encrypted Data Without Decrypting It (Keno Fischer)

• Reliable and Reproducible Binary Artifacts for Julia Packages (Elliot Saba, Stefan Karpinski, Kristoffer Carlsson)

• The Julia Language Participates in Google’s Code-In Contest (Avik Sengupta, Logan Kilpatrick)

• ODE Solver Multi-Language Wrapper Package Work-Precision Benchmarks (MATLAB, SciPy, Julia, deSolve (R)) (Chris Rackauckas)

• NVIDIA Expands Support for Arm with HPC, AI, Visualization Containers on NGC (Chintan Patel)

• Working with Binary Libraries for Optimization in Julia (Mathieu Besançon)

• The Emergent Features of JuliaLang: Part II – Traits (Lyndon White)

• First Approach for the Kaggle Santa 2019 Challenge (Ole Kröger)

• Finding the Maximum Cardinality Matching in a Bipartite Graph (Ole Kröger)

• How to Profile Julia Code? (Ole Kröger)

• New Trends in Programming Languages (Nicolau Leal Werneck)

• Learning Algorithmic Techniques: Dynamic Programming (Eric Hanson)

• Evaluating Arbitrary Precision Integer Expressions in Julia using Metaprogramming (Mohammed El-Beltagy)

• DifferentialEquations.jl v6.8.0: Advanced Stiff Differential Equation Solving (Chris Rackauckas)

• GeoStats.jl v0.10 (Júlio Hoffiman)

• TensorFlow 2.0: Building Simple Classifier Using Low Level APIs (Al-Ahmadgaid B. Asaad)

• Two Weeks With Julia: a Java Programmer’s Journey Into New Paradigms (Seth Chapman)

• How to Install Julia on Ubuntu 18.04 (Andrea Galloni)

• Julia Computing and NVIDIA Bring Julia GPU Computing to Arm (Julia Computing, NVIDIA)

Julia Presentations & Lectures

• You Have Data and I Have Distributions – A Talk on Turing.jl and Bijectors.jl (Tor Erlend Fjelde)

• TIMES Migration Feasibility Study (Antti Lehtilla, Tarun Sharma, Olexandr Balyk, George Giannakidis, Maurizio Gargiulo)

• Mixing Differential Equations and Neural Networks for Physics-Informed Learning (Chris Rackauckas)

Upcoming Julia Events

• Paris: NeuralQuantum.jl: Approaching Quantum Physics with Neural Networks with Filippo Vicentini and Optimization on Manifolds with Antoine Levitt Dec 3

• Warsaw: Praca z Git i GitHub w Procesie Tworzenia Kodu Julia with Bogumił Kamiński and Warszawskie Forum Julia at SGH Warsaw School of Economics Dec 3

• Online: Private Package Management and Governance with Julia with Matt Bauman (Julia Computing) Dec 4

• Montreal: Node + JS Interactive with Jameson Nash (Julia Computing) Dec 11-12

• Paris: Julia Day at Jussieu with Michael Herbst Dec 13

• Louvain-la-Neuve, Belgium: 4th Annual JuMP-dev Workshop June 15-17

• Lisbon: JuliaCon 2020 at the ISCTE – Instituto Universitário de Lisboa July 27-31

Recent Julia Events

• Freiburg: Julia in Freiburg with Konstantinos Michailidis and Julia User Group Freiburg at CoWorking Freiburg Nov 12

• Prague: Julia Meetup Vol 2. – From Basics to Multiple Dispatches with Giuliano Giannetti, Tomáš Oravec and Prague Julia Programming Group Nov 13

• Dublin: Julia Programming Workshop with Paulito Palmes (IBM Research) and ODSC Dublin Data Science at Dublin Talen Garden Nov 16

• Denver: SC19 with Alan Edelman (Julia Computing) Nov 17-22

• London: Open Data Science Conference (ODSC) with Avik Sengupta (Julia Computing) Nov 19-22

• Sydney: Data Science & Analytics using Python, R, Julia + More with Kale Temple and PyData Sydney at Amazon Web Services – Nov 20

• London: Julia – A Fresh Approach to Numerical Computing with Avik Sengtupta (Julia Computing) and Open Source Specialist Group at the British Computer Society London Nov 21

• Berlin: Julia Users Group Fall Workshop with David Higgins and Simon Christ (OpenTechSchool) Nov 23

Julia Jobs, Fellowships and Internships

Do you work at or know of an organization looking to hire Juli programmers as staff, research fellows or interns? Would your employe be interested in hiring interns to work on open source packages that ar useful to their business? Help us connect members of our community t great opportunities by sending us a email, and we’ll get the word out.

There are more than 300 Julia jobs currently listed on Indeed.com, including jobs at Accenture, Airbus, Amazon, AstraZeneca, AT&T, Barnes & Noble, BlackRock, Capital One, CBRE, Charles River Analytics, Citigroup, Comcast, Conde Nast, Cooper Tire & Rubber, Disney, Dow Jones, Facebook, Gallup, Genentech, General Electric, Google, Huawei, Ipsos, Johnson & Johnson, KPMG, Lockheed Martin, Match, McKinsey, NBCUniversal, Netflix, Nielsen, Novartis, OKCupid, Opendoor, Oracle, Pandora, Peapod, Pfizer, Raytheon, Spectrum, Wells Fargo, Zillow, Brown, BYU, Caltech, Dartmouth, Emory, Harvard, Johns Hopkins, Louisiana State University, Massachusetts General Hospital, MIT, Penn State, Princeton, UC Davis, University of Chicago, University of Delaware, University of Kentucky, UNC-Chapel Hill, USC, University of Virginia, Argonne National Laboratory, Federal Reserve Bank, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, National Renewable Energy Laboratory, Oak Ridge National Laboratory, Pacific Northwest National Laboratory, State of Wisconsin and many more.

Contact Us: Please contact us if you wish to:

• Purchase or obtain license information for Julia products such as JuliaSure, JuliaTeam, or JuliaRun

• 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, fellowship 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 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 more than 11 million times 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. 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.

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.

Cambridge, MA – Julia Computing co-founder and Chief Scientist Alan Edelman will be a TEDxMIT speaker on Friday December 6 as part of the TEDxMIT Operation Earth event. The subject of his talk is ‘Julia’s Role in Healing the Earth’.

Alan Edelman is a co-creator of Julia, MIT Professor of Applied Mathematics, Principal Investigator at the MIT Julia Lab and winner of the Sidney Fernbach Award, Charles Babbage Prize, Gordon Bell Prize and many other awards.

For more information, please visit TEDxMIT.org.

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 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 more than 11 million times 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.

Julia Computing was founded in 2015 by all the creators of Julia to provide products including JuliaTeam, JuliaSure and JuliaRun to businesses and researchers using Julia.