The challenge of bringing projects from research to real world impact is spinning out of control. Ideas need to reach clusters and super-computers for large scale data; be deployed to the cloud for real-time analysis, and be built on by other industrial and academic projects. Adhoc solutions by fragmented research groups using different languages create sprawling code bases and myriad tools that impair collaboration and innovation. Meanwhile, the well-oiled machines of Google (Deepmind), Facebook and others loom large over academic initiatives.
The Julia language offers a compelling solution: a single system that can meet all of these demands at once, cutting down the innovation cycle by months or years. Julia’s academic users have conquered some of the biggest data analysis problems with smaller teams and tighter budgets. This session will explore Julia’s impact from self-driving cars to super-computing in astronomy, as well as covering the cutting edge tools that the community is developing for mathematical modelling and machine learning.
Sebastian Vollmer is leading the effort to create a special interest group for Julia at Turing.
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