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Data Science Webinar Series - Sanjay Padhi - Apr 13

Evolution of Predictive Analytics: Machine Learning with AWS

April 13, 2017 11:00 AM  to 
April 13, 2017 12:00 PM
NSF Room 110

One of the most explored features of Big Data is predictive analytics. Predictive analytics is a set of techniques that are fundamental to large organizations like Amazon. Methods such as Machine Learning are used in many aspects of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Analytics on Big Data has given rise to various “smart” projects, such as Connected Intersections, Smart Cities, and Smart Health. This talk will provide a range of such case studies using predictive analytics including detailed overview of methods such as Machine Learning and Deep Learning using AWS. Supervised and unsupervised based learning frameworks and its implications in the fields of Agriculture, Scientific Computing, Medical Imaging, Cancer detection, Diabetic Retinopathy, and Voice-enabled solutions to improve management of chronic disease will be discussed. Deep learning frameworks associated with image, text and natural language processing will be outlined. Amazon Artificial Intelligence (AI) framework including AI services, platform and engine will be discussed. Potential areas for collaboration with the AWS Research Initiative will also be presented.
Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives with the National Science Foundation. Dr. Padhi has more than 15 years of experience in large-scale distributed computing, Data Analytics and Machine Learning. He is the co-creator Workload Management System, currently used for all the data processing and simulations activities by CMS, one of the largest experiments in the world at CERN, consisting of more than 180 institutions across 40 countries. He also co-founded the ZEUS Computing Grid project at Deutsches Elektronen-Synchrotron (DESY), Germany before joining CERN. Sanjay obtained his Ph.D from McGill University in High Energy Physics and is also currently appointed by the Dean of Faculty as an Adjunct Associate Professor of Physics at Brown University.

Due to unforeseen circumstances, we will not be able to stream this event.

This event is part of Webinars/Webcasts.

Meeting Type

Elena Zheleva, email:

NSF Related Organizations
Directorate for Computer & Information Science & Engineering