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Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
Ananth Sankar

Ananth Sankar
Sr. Director | AI Technical Acceleration and Scaling, Intel

Ananth Sankaranarayanan serves as head of Technical Acceleration and Scaling in the AI Products Group at Intel. In this role, he is 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. Prior to his current role, Ananth led the creation of Big Data Analytics Solutions team and championed multiple high growth industry-first solutions in Intelligent Transportation, Health-Care, Retail, Financial Services segments and drove them to a worldwide scale. Ananth won “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 graduated with a Bachelor of Engineering in Computer Science and earned 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 2 patents, has co-authored more than 10 publications and a book on “AI for Autonomous Networks” proceeds of which go to “Girls who code” non-profit organization.


14:35–15:15 Thursday, 11 October 2018
Location: King's Suite - Sandringham
Secondary topics:  Edge computing and Hardware, Platforms and infrastructure
Ananth Sankar (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.