Abstract:

Bio: Irene Qualters serves as the Associate Laboratory Director for Simulation and Computation. She previously served as a Senior Science Advisor in the Computing and Information Science and Engineering (CISE) Directorate of the National Science Foundation (NSF), where she contributes to strategic leadership in new directions for the CISE Directorate. In her nearly nine years at NSF, she has had responsibility for developing NSF’s vision and portfolio of investments in high performance computing, and has played a leadership role in interagency, industry, and academic engagements to advance computing. Irene also served on the Science and Technology Committee of the LLNS/LANS Board of Governors. Prior to her NSF career, Irene had a distinguished 30-year career in industry, with a number of executive leadership positions in research and development in the technology sector. During her 20 years at Cray Research, she was a pioneer in the development of high performance parallel processing technologies to accelerate scientific discovery. Subsequently as Vice President, she led Information Systems for Merck Research Labs, focusing on international cyberinfrastructure to advance all phases of pharmaceutical R&D. Irene has a M.S. in Computer Science from the University of Detroit and a B.S. from Duquesne University.

Abstract:FPGAs are now more readily available as general purpose compute devices, enabled by new development and investment in FPGA acceleration. Researchers, software developers, and data scientists can leverage Programmable Accelerator Cards, qualified open-source driver stacks, and high-level programming languages to accelerate custom algorithms, database accesses, image inferencing, genomics, and more.

Bio: Jeff has been with Altera/Intel for 19 years holding both technical and technical sales role positions and prior to Altera Jeff was a design engineer at Rockwell Collins designing avionics for commercial aircraft focusing on embedded systems. He is also a college lecturer teaching Digital Logic Lab at the University of Wisconsin-Milwaukee and earned his Master’s degree in Electrical and Computer Engineering from the University of Iowa in 1993. In his free time he host the weekly www.stemonfire.com podcast and enjoys pretty much anything outdoors.

Abstract: NSF's Directorate for Computer and Information Science and Engineering (CISE) seeks to expand the frontiers of computing, communication, and information technologies; support advanced data and computing capabilities across all fields of science and engineering; and help prepare a workforce with the skills and competencies necessary for the 21st-century digital economy. This talk will present an overview of CISE and its programs and investments, along with a vision for evolving programs and priorities to support the frontiers of the field, and of science and engineering more broadly.

Bio:Ken Calvert is Division Director for Computer and Network Systems in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation. He is there on temporary assignment (since May 2016) from the University of Kentucky, where he is Gartner Group Professor in Network Engineering and former Chair of the Department of Computer Science. His research interests include future Internet architecture, software-defined networking, and network security. He received his Ph.D. in computer science from the University of Texas at Austin. He holds a M.S. in computer science from Stanford University and a B.S. in computer science and engineering from the Massachusetts Institute of Technology. Prior to his appointment at the University of Kentucky, he was a Member of the Technical Staff at Bell Laboratories in Holmdel, NJ, and served on the faculty in the College of Computing at the Georgia Institute of Technology. He is an IEEE Fellow and a member of the ACM.

Abstract: 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, education, financial modeling, 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 studies using predictive analytics including detailed overview of methods such as Machine Learning (ML) and Deep Learning using AWS. Fully managed Artificial Intelligence (AI) services to help researchers build, train and deploy ML models in various domains including Computer Vision and Natural Language Processing (NLP) will also be outlined. Supervised and unsupervised based learning frameworks and its implications in the fields of Scientific Computing, Medical Imaging, Cancer detection, Diabetic Retinopathy, and Voice-enabled solutions to improve management of chronic disease will be discussed. Collaborations on research with funding agencies such as the NSF and NIH (including NIH STRIDES), as well as AWS Research Credit program will also be outlined.

Bio: 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 of the Workload Management System currently used for all the data processing and simulations 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 Professor of Physics at Brown University.