New York, NY – What do the following companies and organizations all have in common?

Apple, Amazon, Facebook, BlackRock, Ford, Oracle, Comcast, Massachusetts General Hospital, Farmers Insurance, Los Alamos National Laboratory and the National Renewable Energy Laboratory

They are all looking to hire Julia programmers in 2017.

“In the last quarter of 2016 and already in the first quarter of 2017, there is an explosion in the number of job postings for skilled Julia programmers,” said Viral Shah, Julia Computing CEO. “While 2016 showed tremendous growth among early adopters, 2017 is shaping up to be the breakout year for Julia adoption.”

Julia is the fastest modern high performance open source computing language for data and analytics. It combines the functionality and ease of use of R and Python with the speed of Java and C++. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity.

1. Julia is lightning fast. Julia provides speed improvements up to 1,000x for insurance model estimation, 225x for parallel supercomputing image analysis and 11x for macroeconomic modeling.
2. Julia is easy to learn. Julia’s flexible syntax is familiar and comfortable for users of Python and R.
3. Julia integrates well with existing code and platforms. Users of Python, R and other languages can easily integrate their existing code into Julia.
4. Elegant code. Julia was built from the ground up for mathematical, scientific and statistical computing, and has advanced libraries that make coding simple and fast, and dramatically reduce the number of lines of code required – in some cases, by 90% or more.
5. Julia solves the two language problem. Because Julia combines the ease of use and familiar syntax of Python and R with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language. This saves time and reduces error and cost.

Julia users and partners include: Amazon, IBM, Intel, Microsoft, DARPA, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Moore Foundation, Nobel Laureate Thomas J. Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX and many of the world’s largest investment banks, asset managers, fund managers, foreign exchange analysts, insurers, hedge funds and regulators. Julia is being used to analyze images of the universe and research dark matter, drive parallel computing on supercomputers, diagnose medical conditions, manage 3D printers, build drones, improve air safety, provide analytics for foreign exchange trading, insurance, regulatory compliance, macroeconomic modeling, sports analytics, manufacturing and much, much more.

Julia Computing was founded in 2015 by the co-creators of the Julia language to provide support to businesses and researchers who use Julia.

Julia Computing is pleased to announce a new live Julia training course being taught online on Thursday, January 26, 2017 from 8-11 am EST.

This live online course is available at no charge with your free 10 day trial subscription to O’Reilly Media’s proprietary Safari platform for online learning. Space is limited and registration closes on Thursday, January 19 at 6 pm EST.

This is the first live Julia training course being offered in partnership with O’Reilly Media, the leader in technology instruction, and more such courses are being developed. Other online Julia courses are also available through Coursera and Udemy.

The course is taught by Alan Edelman, Professor of Mathematics at MIT, co-founder of Julia Computing, co-creator of Julia, and Principal Investigator of JuliaLab@MIT.

The course schedule, registration and other information is available here.

About Julia, Professor Alan Edelman and Julia Computing

Julia is the fastest modern high performance open source computing language for data and analytics. It combines the functionality and ease of use of R and Python with the speed of Java and C++ to deliver dramatic improvements in simplicity, speed, capacity and productivity.

1. Julia is lightning fast. Julia provides speed improvements up to 1,000x for insurance model estimation, 225x for parallel supercomputing image analysis and 11x for macroeconomic modeling.
2. Julia is easy to learn. Julia’s flexible syntax is familiar and comfortable for users of Python and R.
3. Julia integrates well with existing code and platforms. Users of Python, R and other languages can easily integrate their existing code into Julia.
4. Elegant code. Julia was built from the ground up for mathematical, scientific and statistical computing, and has advanced libraries that make coding simple and fast, and dramatically reduce the number of lines of code required – in some cases, by 90% or more.
5. Julia solves the two language problem. Because Julia combines the ease of use and familiar syntax of Python and R with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language. This saves time and reduces error and cost.

Julia users and partners include: Amazon, IBM, Intel, Microsoft, DARPA, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Moore Foundation, Nobel Laureate Thomas J. Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX and many of the world’s largest investment banks, asset managers, fund managers, foreign exchange analysts, insurers, hedge funds and regulators. Julia is being used to analyze images of the universe and research dark matter, drive parallel computing on supercomputers, diagnose medical conditions, manage 3D printers, build drones, improve air safety, provide analytics for foreign exchange trading, insurance, regulatory compliance, macroeconomic modeling, sports analytics, manufacturing and much, much more.

Alan Edelman is Professor of Applied Mathematics at MIT, co-founder of Julia Computing, co-creator of Julia, and Principal Investigator of JuliaLab@MIT. In 2004, Edelman founded Interactive Supercomputing (recently acquired by Microsoft). He received the B.S. & M.S. degrees in mathematics from Yale in 1984, and the Ph.D. in applied mathematics from MIT in 1989 under the direction of Lloyd N. Trefethen. Following a year at Thinking Machines Corp and at CERFACS in France, Edelman went to U.C. Berkeley as a Morrey Assistant Professor and Lewy Fellow, 1990-93. He joined the MIT faculty in applied mathematics in 1993. Edelman’s research interests include high performance computing, numerical computation, linear algebra and stochastic eigenanalysis (random matrix theory). He has consulted for Akamai, IBM, Pixar, and NKK Japan among other corporations. A Sloan fellow, Edelman received an NSF Faculty Career award in 1995. He has received numerous awards, among them the Gordon Bell Prize and Householder Prize (1990), the Chauvenet Prize (1998), the Edgerly Science Partnership Award (1999), the SIAM Activity Group on Linear Algebra Prize (2000), and the Lester R. Ford Award, (2005). In 2011, Edelman was selected a Fellow of SIAM, for his contributions in bringing together mathematics and industry in the areas of numerical linear algebra, random matrix theory, and parallel computing.

Julia Computing was founded in 2015 by the co-creators of the Julia language to provide support to businesses and researchers who use Julia.

Nashville, TN – Julia Computing is pleased to congratulate Iain Dunning, Joey Huchette and Miles Lubin for winning the 2016 INFORMS Computing Society (ICS) Prize for the Julia JuMP optimization package.

JuMP is used today by thousands of engineers, statisticians, physicists, economists, data scientists and other researchers to model constrained optimization problems faster and more efficiently using Julia, the high performance open source computing language that combines the functionality and ease of use of R and Python with the speed of C++.

JuMP has been cited for applications in train scheduling, self-driving cars, electric vehicle charging, power grid control, plasma physics and fantasy sports. Industrial users include Thales Canada and PSR Energy Consulting.

The ICS Prize is awarded at the INFORMS annual conference for the best paper or group of papers dealing with the interface between Operations Research and Computer Science. The paper which introduces JuMP is available here.

The prizewinners, Iain Dunning, Joey Huchette and Miles Lubin, are all current or former students at the MIT Operations Research Center, where they developed JuMP as a student-led project. Iain Dunning is currently an artificial intelligence researcher at DeepMind Technologies.

According to the ICS Prize Committee:
“JuMP is a Julia-language based modeling language that allows users to express a wide variety of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a convenient algebraic syntax. JuMP’s design leverages advanced features of the Julia language to offer distinctive functionality while achieving performance in instance creation often similar to commercial modeling tools. Powerful features of JuMP that make it an attractive tool for optimization tasks include implementation of callbacks for modifying the branch-and-bound algorithm, automatic differentiation of user-defined nonlinear functions, and easy-to-develop add-ons for specialized problem classes. The modular design has enabled many third-party extensions for more specialized optimization problem classes. Specifically, JuMP can be easily used to embed optimization problems as part of a complex algorithmic control structure, such as in decomposition methods. In just two years since its creation, JuMP has had a significant impact in the computational optimization community. JuMP is used to teach optimization in more than a dozen courses around the world. JuMP has been embedded in packages for important applications in engineering, statistics, and data analysis.”