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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
Ananth Sankaranarayanan

Ananth Sankaranarayanan
Senior Director, AI Technical Acceleration and Scaling, Intel

@IntelAI

Ananth Sankaranarayanan is a senior director of AI technical acceleration and scaling at Intel, where he’s responsible for leading a worldwide team that enables customers and partners to build AI solutions with the maximum benefit of Intel hardware and software capabilities. Previously, he led the creation of the big data analytics solutions team and championed multiple high-growth industry-first solutions in intelligent transportation, healthcare, retail, and financial services segments and drove them to a worldwide scale. Ananth won the “Intel Achievement Award,” the highest employee recognition, for his transformative work in the creation of high-performance computing (top 500 supercomputer) production capability to accelerate silicon design and manufacturing. Ananth earned his bachelor’s of engineering in computer science and his master’s degree in business administration from the City University of Seattle. Ananth is a strong supporter of improving education, and as a portion of his volunteer work, he coaches middle and high school robotics and STEM teams who have won national-level recognitions for engineering and teamwork. Ananth holds two patents and has coauthored more than 10 publications and a book, AI for Autonomous Networks, the proceeds of which go to the Girls Who Code nonprofit organization.

Sessions

14:35–15:15 Thursday, 11 October 2018
Location: King's Suite - Sandringham
Secondary topics:  Edge computing and Hardware, Platforms and infrastructure
Ananth Sankaranarayanan (Intel), Valeriu Codreanu (SURFsara), Damian Podareanu (SURFsara), Colin Healy (Dell EMC)
SURFSara and Intel collaborated as part of the Intel Parallel Computing Center initiative to advance the state of large-scale neural network training on Intel Xeon CPU-based servers. Ananth Sankar, Valeriu Codreanu, Damian Podareanu, and Steve Smith share insights on several best-known methods for neural network training and present results from tests performed on Stanford's CheXNet project. Read more.