Industry Session
Industry Session I
Speaker : Amod Anandkumar
Date : 20 December, 2016
Title : What's New in MATLAB for Image Processing & Computer Vision
Bio: Amod Anandkumar is the Team Lead for Signal Processing and Communications at MathWorks India. Prior to this, he was a Lead Engineer with the Advanced Technology Group at Samsung Research India, Noida for 1.5 years where he developed physical layer techniques for LTE wireless communication systems and novel healthcare applications for smartphones. He was also a Post-Doctoral Research Fellow at the Biomedical Signal Analysis Lab, GE Global Research Bangalore working on advanced beamforming techniques for ultrasound imaging and novel signal processing solutions for ICU patient monitoring systems, resulting in one US patent filing. Amod holds a BTech degree from National Institute of Technology Karnataka and a PhD degree from Loughborough University, UK. His research interests include applied signal processing, next-generation wireless networks, computer vision, game theory, and convex optimization. He has published and reviewed papers in numerous international conferences and journals.
Industry Session II
Speaker : Sundara R Nagalingam
Date : 20 December, 2016
Title : GPU accelerated Deep Learning
Bio : Sundara R Nagalingam is the Head of Deep Learning practice for NVIDIA India. He is a mechanical engineer and has a master’s in management. He has twenty years of experience in solutions involving Visual Computing, Virtualization and High Performance Computing. He also has exposure to the work cultures of multiple countries in the Asia Pacific region. He has a strong technical background and his areas of interest include Deep Learning, Big Data Analytics, IoT and Automotive Solutions.
GPU accelerated Deep Learning
Abstract : The talk will provide an over view of how Deep learning has evolved as the fastest-growing field in artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text. Today's deep learning solutions rely almost exclusively on NVIDIA GPU-accelerated computing to train and speed up challenging applications such as image, handwriting, and voice identification. The talk will focus on how GPU accelerated Deep Learning has evolved in the recent times with focus on deep learning software, libraries and tools.
