Abstract:

I will discuss a number of signal processing aspects of big data through examples. The first issue is efficient data acquisition (i.e., where to sample) through active learning, which is illustrated by a fine-grained indoor localization technique with adaptively sampled RF fingerprint. The second issue is low-rate sampling (i.e., how to sample) for distributed information extraction, illustrated by a cooperative spectrum sensing system. Then I will discuss statistical inference for big data based on sequential Monte Carlo through an example of gene binding site discovery. Finally, I will briefly discuss the issue of time-data tradeoff, i.e., given a massive dataset how to deliver an answer to an inferential question within a certain time budget.

Biography:

Xiaodong Wang received the Ph.D degree in Electrical Engineering from Princeton University. He is a Professor of Electrical Engineering at Columbia University in New York and a Visiting Chair Professor at Shanghai Jiao Tong University. Dr. Wang’s research interests fall in the general areas of signal processing and communications, and has published extensively in these areas. Among his publications is a book entitled “Wireless Communication Systems: Advanced Techniques for Signal Reception”, published by Prentice Hall in 2003. His current research interests include wireless communications, statistical signal processing, and genomic signal processing. Dr. Wang received the 1999 NSF CAREER Award, the 2001 IEEE Communications Society and Information Theory Society Joint Paper Award, and the 2011 IEEE Communication Society Award for Outstanding Paper on New Communication Topics. He has served as an Associate Editor for the IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications, the IEEE Transactions on Signal Processing, and the IEEE Transactions on Information Theory. He is a Fellow of the IEEE and listed as an ISI Highly-cited Author.