ICDIS 2018

Chaitan Baru, PhD

Senior Advisor for Data Science, CISE,

National Science Foundation

Speech title

Harnessing the Data Revolution

Abstract: Harnessing the Data Revolution (HDR) is one of NSF's six "Big Research Ideas," aimed at supporting fundamental research in data science and engineering; developing a cohesive, federated approach to the research data infrastructure needed to power this revolution; and developing a 21st-century data-capable workforce. HDR will enable new modes of data-driven discovery allowing researchers to ask and answer new questions in frontier science and engineering, generate new knowledge and understanding by working with domain experts, and accelerate discovery and innovation. This initiative builds on NSF's strong history in data science investments. We will describe NSF’s vision for the HDR Big Idea, as well as related community activities, including workshops on translational data science, an open knowledge network, and the idea of a Data Science Corps. As the only federal agency supporting all fields of science and engineering, NSF is uniquely positioned to help ensure that our country's future is one enriched and improved by data.

Short bio: Chaitan Baru is Senior Advisor for Data Science in the Computer and Information Science & Engineering Directorate at the National Science Foundation, Alexandria, VA. He co-chairs the NSF working group for the Harnessing the Data Revolution Big Idea, and has responsibility for the cross-Foundation BIGDATA research program. He is advisor to the NSF Big Data Regional Innovation Hubs and Spokes program (BD Hubs/Spokes) and was engaged in the development of the NSF Transdisciplinary Research in Principles of Data Science (TRIPODS) program. He co-chairs the Big Data Interagency Working Group—which is part of the Networking and IT R&D program of the National Coordination Office, White House Office of Science and Technology Policy—and is a primary co-author of the Federal Big Data R&D Strategic Plan (released May 2016). 


He is on assignment at NSF from the San Diego Supercomputer Center (SDSC), University of California San Diego, where he is a Distinguished Scientist and Director of the Advanced Cyberinfrastructure Development Group (acid.sdsc.edu) and the Center for Large-scale Data Systems Research (CLDS).

C.-C. Jay Kuo, PhD

Director of the Media Communications Laboratory,

University of Southern California 

Speech Title 

Abstract: The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in many applications such as image classification, detection and processing. Yet, the CNN solution has its own weaknesses such as robustness against perturbation, scalability against the class number and portability among different datasets. Furthermore, CNN’s working principle remains mysterious. In this talk, I will first explain the reasons behind the superior performance of CNNs. Then, I will present an alternative solution, which is motivated by CNNs yet allows rigorous and transparent mathematical treatment, based on a data-driven Saak (Subspace approximation with augmented kernels) transform. The kernels of the Saak transform are derived from the second-order statistics of inputs in a one-pass feedforward way. Neither data labels nor backpropagation is needed in kernel determination. The pros and cons of CNNs and multi-stage Saak transforms are compared.

Short bio: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 140 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. Dr. Kuo is a co-author of 260 journal papers, 900 conference papers, 30 patents and 14 books.

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