RCE an HPC Podcast
Brock Palen and Jeff Squyres speak with the creators of Academic Torrents a distributed system for sharing enormous datasets - for researchers, by researchers. The result is a scalable, secure, and fault-tolerant repository for data, with blazing fast download speeds.
Joseph Paul Cohen is a postdoctoral fellow at the Montreal Institute for Learning Algorithms (MILA) in the University of Montreal. He obtained a Ph.D Degree in computer science from the University of Massachusetts Boston in 2016. His research interests include machine learning, computer vision, ad-hoc networking, and cyber security. Joseph received a U.S. National Science Foundation Graduate Fellowship in 2013. Joseph is the founder and director of the Institute for Reproducible Research (a U.S. 501©3 non-profit) which produces tools for researchers such as AcademicTorrents.com and ShortScience.org. He is also the creator of BlindTool (a mobile application that uses artificial intelligence to provide a sense of vision to the blind) and Blucat (netcat for Bluetooth). He has worked in industry for small startups, large corporations, government research labs, educational museums, as well as been involved in projects sponsored by NASA and the DOE.
Henry Z. Lo is currently a Senior Data Analyst at McKinsey and Company working on deep learning solutions. He obtained a PhD from the University of Massachusetts Boston Computer Science department. During his studies he received a McNair fellowship, a Sanofi Genzyme fellowship, the Randall G. Malbone award for academic achievement, and an NSF EAPSI award to study in Shanghai, China. Before graduating, Henry partnered with Joseph Paul Cohen to start a non-profit to promote accessibility in science.
Brock Palen and Jeff Squyres speak with the creators of Julia. Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.
Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral Shah are all co-creators of the Julia language. They are also co-founders of Julia Computing, Inc., a company that builds products for data scientists to accelerate the cycle of innovation, from discovery to production. Their first blog post announcing Julia to the world captures the essence of what they set out to do.
Julia is a modern and easy to use high performance programming language. Parallel computing is fundamental to Julia rather than being an afterthought. It is a vibrant open source project with a diverse community of 500 contributors around the world. Research on Julia is anchored at Alan Edelman’s Julia Lab at MIT. The Julia community has contributed over 1,000 open source packages to date. A number of universities and MOOCs use Julia for teaching and research. It is also used by businesses in areas as diverse as finance, engineering, aerospace, automotive, robotics, healthcare, and e-commerce, to name a few. All these applications and research have been presented over time in four JuliaCons held over the last several years in the US and India.
Prof. Alan Edelman: http://www-math.mit.edu/~edelman/
Alan Edelman is a professor of applied mathematics and a member of the Computer Science and AI Laboratories at MIT. He has won numerous prizes including the Gordon Bell Prize, the Householder prize, various SIAM and AMS prizes, and is a fellow of SIAM and the AMS. He was CTO of Interactive Supercomputing, a startup in the area of software for high performance and big data computing, which was later acquired by Microsoft. He has consulted or worked for companies such as Microsoft, Akamai, Pixar, IBM, and others most recently working on numerical verification. Before that he worked on “big data” analysis tools, even before “big data” became a household term. He currently leads the MIT group on the Julia project as well as working on practical algorithms and theoretical mathematics.
Dr. Viral B. Shah: https://www.linkedin.com/in/viralbshah
Viral Shah is computer scientist with a keen interest in the interaction of technology with public policy. He has had a long-term track record of building open-source software. Apart from Julia, he is also co-creator of Circuitscape, an open-source program which borrows algorithms from electronic circuit theory for ecological conservation. Prior to founding Julia Computing, he founded FourthLion Technologies in India to build India’s first data-driven political campaigns. In the Government of India, he was an early member of the country’s national ID project - Aadhaar, where his work on re-architecting India’s social security systems led to a significant increase in social and financial inclusion, while simultaneously saving the exchequer over a billion dollars in slippage. The experiences of implementing technology at such scale for a billion people are collected in his book: Rebooting India. Viral has a Ph. D. from the University of California at Santa Barbara, in Computer Science.
Stefan Karpinski: https://www.linkedin.com/in/stefankarpinski
Prior to founding Julia Computing, Stefan previously worked as a software engineer and data scientist at Akamai, Citrix Online, and Etsy. In addition to running Julia Computing, he has a part-time appointment as a Research Engineer at New York University as part of the Moore-Sloan Data Science Initiative. Stefan received a B.A. in mathematics from Harvard University in 2000.
Dr. Jeff Bezanson: https://www.linkedin.com/in/jeffbezanson
Jeff Bezanson is a serial programming language designer. Prior to designing Julia, Jeff wrote compilers at Interactive Supercomputing. He is also the author of a particularly tiny Scheme implementation called femtolisp. He is an alumnus of the Massachusetts Institute of Technology and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where his thesis was centred on building high performance dynamic languages for technical computing. He received a B.A. in computer science from Harvard in 2004, and a PhD from MIT in 2015.