Introducing Julia: A Programming Language for Data Scientists
Friday, February 1st from 6:00 - 8:00pm
Jeff Bezanson, Stefan Karpinski & Viral Shah, Developers, Julia
Data scientists, and the mathematical programming languages that support them, are changing the world but they are suffering from growing pains. Technical computing languages that have been around for decades are slow to adopt new compiler technologies such as JIT, optional type indications, and others.
Julia is a fast, simple, and dynamic open source programming language for high-performance technical computing. Julia integrates best-of-breed C and Fortran libraries for statistics, linear algebra, signal processing, and random number generation. It's also designed out of the box for parallel computing, meaning it's easy to scale computation across multiple processers in the cloud.
This session will demonstrate some of the special capabilities of Julia and give you the tools you need to get started. The developers will explain why Julia is a good choice for data scientists, show simple problems, and talk about the future direction of the language. Presentation will be followed by Q&A and discussion.
Stefan Karpinski is one of the co-creators and core developers of the Julia language. He is an applied mathematician and data scientist by trade, having worked at Akamai, Citrix Online, and Etsy, but currently is focused on advancing Julia's design, implementation, documentation, and community.
Jeff Bezanson has been developing the Julia language for three years. Previously, he worked as a software engineer at Interactive Supercomputing, which developed the Star-P parallel extension to MATLAB. At the company, Jeff was a principal developer of "M#", an implementation of the MATLAB language running on .NET.
Viral Shah is one of the co-creators and core developers of the Julia language. Viral's interests revolve around "all things High Performance Computing". In addition to working on Julia, Viral is involved with the development of Circuitscape an innovative program that borrows from circuit theory to predict genetic diffusion among populations.
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