Abstract

Data is growing very fast. Today one can spot business trends, detect environmental changes, predict forthcoming social agendas and combat crime, by analyzing large data sets. However, this so-called “Big Data” analytics is challenging because they have unprecedentedly large volumes. In this presentation, we describe a new approach based on the recent theory of compressive sensing to address the issue of processing, transporting and storing large data sets of enormous sizes gathered from high-resolution sensors and the Internet.

Speaker Bio

Prof. H T Kung

William H. Gates Professor of Computer Science and Electrical Engineering, Harvard University

Professor H. T. Kung is William H. Gates Professor of Computer Science and Electrical Engineering at Harvard University. He is interested in computing, communications and sensing, with a current focus on wireless networking, compressive sensing, and biologically inspired object detection and recognition. Prior to joining Harvard in 1992, he taught at Carnegie Mellon University for 19 years after receiving his Ph.D. there. Professor Kung has pursued a variety of research interests in his career, including complexity theory, database systems, VLSI design, parallel computing, computer networks, network security, wireless communications, and networking of unmanned aerial systems. To complement his academic activities, Professor Kung maintains a strong link with industry. He has served as a consultant and board member to numerous companies and government organizations. Professor Kung’s professional honors include: Member of the National Academy of Engineering, Member of the Academia Sinica (in Taiwan), and Guggenheim Fellowship.

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• Tackling “big data” volumes with compressive sensing