[Seminar] Big Data and Compressive Sensing
Data is growing very fast. Today by analyzing large data sets, one can spot business trends, detect environmental changes, predict forthcoming social agendas and combat crime. This so-called "Big Data” analytics is challenging. For processing, transporting and storing large data sets of enormous sizes, data will in general need to be greatly compressed, especially for mobile applications. Large data statistics will need to be highly carefully examined to allow correct conclusions. It would be desirable to process data in compressed domain. All these mean we need to bring not only an unprecedentedly large amount of computing power to bear, and but also a set of new approaches and methodologies in systems and analysis. In this presentation, we will give a brief overview of these large issues related to big data, and some compressive sensing based approaches under study at Harvard and elsewhere.
Member of the National Academy of Engineering, Member of the Academia Sinica, Guggenheim Fellowship, etc.
1974~1992 Professor at Carnegie Mellon University, 1992~Now William H. Gates Professor at Harvard University,
1968 - B.S. from National Tsing-Hua University, 1974 - Ph.D. from Carnegie Mellon University,
Research Interests Computing, communications and sensing, parallel, computing, computer networks, etc.
● 문의 : 서울대-삼성전자 SW 공동연구센터 (02-880-7259) http://cic.snu.ac.kr