Who is using the Julia Programming Language?

Discover why an increasing number of companies and professionals are turning to Julia to develop their data projects

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If you work in data science, you will be fully familiar with Python and R. To date, these two programming languages are ruling the data world, and you won’t get very far in your data career without at least one of them. However, data science is a rapidly-evolving field, with new tools and software popping up every day. Among the most promising developments is Julia programming language.

Despite its young age (the first version was released in 2012 with 1.0 coming later in 2018), Julia has already impressed the world of numerical computing. Sometimes referred to as the inheritor of Python and R, Julia blends together the best of the two programming languages, resulting in an ideal language for scientific computing, machine learning, and data mining.

In the ten years since its first launch, Julia has been steadily gaining maturity. The numbers speak by themselves. Since its first release, Julia has been downloaded over 40 million times, and the Julia community has registered over 8,000 Julia packages for community use. As for its popularity among all other languages, Julia ranks a decent 25th and 27th position in the October 2022 PYPL Index and TIOBE Index, respectively.

Given its great potential, it’s not surprising that a growing number of major organizations are using Julia. Keep reading to discover some of the most interesting uses of Julia in data science!

Best Julia use cases

The popularity of Julia is on the rise. Over 10,000 companies across industries are leveraging the power of Julia to develop their data projects. Given its high performance and speed, Julia is particularly well-suited for computer-intensive tasks. Here is a list of the best use cases for Julia in different sectors of the economy.

Finance

Several companies in the financial sector are using Julia.

• BlackRock, the world’s largest asset manager, has written analytics modules for its flagship product Aladdin in Julia, and uses the language for several time series data analytics and big data applications.
• The Federal Reserve Bank of New York uses Julia to conduct some of its macroeconomic models.
• Nowcasting Economics is using Julia to reduce macroeconomic processing from weeks to days.

Energy

Companies working in the energy sector are using Julia to optimize energy investments and protect the electricity infrastructure.

• PSR has switched to Julia to develop analytical tools for studying and simulating energy market behavior.
• AOT Energy uses Julia to quickly identify investment opportunities in energy commodities, such as petroleum, petrochemicals, coal, natural gas, and renewables.
• Fugro Roames has developed a Julia-powered machine learning model to identify failures in the electricity grid.

Aerospace

The aerospace industry has been one of the earliest adopters of Julia.

• The Brazilian National Institute for Space Research uses Julia to plan space missions.
• The Federal Aviation Administration is using Julia to develop a next-generation collision avoidance system.
• The Celeste team, comprising researchers from MIT, Harvard, and UCBerkeley, are using Julia to speed astronomical image analysis, and catalog astronomical objects.

Climate Science

Julia’s high performance makes it the perfect candidate to model complex scenarios in the field of climate science.

• The Climate Modeling Alliance chose Julia for their next-generation global climate model. This multi-million dollar project aims to build an earth-scale climate model providing insight into the effects and challenges of climate change.
• NASA is supporting the development of circuitscape.jl, a Julia package designed to model animal movement across landscapes.
• Climate scientists and glaciologists are using Julia to accurately measure ice thickness in glaciers.

Medical

Julia is powering cutting-edge techniques to diagnose and treat diseases.

• IBM is using neural network algorithms trained with Julia to diagnose diabetic retinopathy.
• Researchers in the UK are using Julia to model tumor evolution and support cancer treatment decision-making.
• Researchers from the University of Vienna are using training machine learning models with Julia to identify disease patterns and prioritize difficult cases.

Pharmaceutical

Julia is particularly popular among top-class pharmaceutical companies.

• Pfizer are using Julia to accelerate simulations of new therapies for metabolic diseases.
• AstraZeneca researchers are leveraging Julia packages, such as Flux.jl and Turing.jl, to predict toxicity with a Bayesian neural network.
• United Therapeutics has built a computational model of the lung with Julia to develop treatments for rare diseases.

Research

Universities and institutions around the world are using Julia to advance cutting-edge research in all kinds of disciplines.

• MIT researchers are leveraging Julia to develop robots that are able to climb stairs and walk through irregular terrain.
• Los Alamos National Laboratory is using Julia to predict and mitigate the impact of extreme events on the delivery of energy commodities.
• UC Berkeley researchers are using Julia to optimize model predictive control in electric cars.

Conclusion

While Python and R are expected to keep dominating the data space in the coming years, Julia is steadily gaining momentum. Julia’s main downside is its youth –the language still has a relatively small community, and doesn’t have as many libraries as its main competitors–, but there are compelling reasons to keep an eye on its evolution.

Against this backdrop, you may be asking yourself: is Julia worth learning? The answer is yes! Given its unique strengths, it is likely that more and more companies will use Julia in the future. As Ari Joury claims in her post, Julia could be your golden ticket to the future if you start learning Julia now.

Until the reign of Julia comes, if you have already mastered Python or R, learning Julia is a smart move, as it can differentiate you from other candidates equipped with the standard data science toolkit.