2017-06-15    Prof. Zhi Tian

Compressive Covariance Sensing Beyond Sparsity

 Title of Lecture: Compressive Covariance Sensing Beyond Sparsity

Lecturer:  Prof. Zhi Tian, George Mason University

Time:  16：00

Date:  2017-06-15

Hosted by: School of Computer Science

 About the Lecture:

Compressive signal sampling is one of the recent important advances in signal processing and statistical learning, with impact to various applications including data sciences, communications, sensor networks, and medical imaging. It requires information-bearing signals to be sparse over known domains, either naturally or by design. In this talk, I will introduce the fresh notion of compressive covariance sensing (CCS), and advocate its exciting implications for applications involving second-order statistics such as covariances and spectra in various domains, even when the underlying signals are not sparse. The CCS framework leverages the underlying structure of relevant covariance statistics beyond sparsity to enable signal compression. It offers a new look at (sparsity-agnostic) compressive sampling for random processes, for which only the signal statistics are meaningfully defined. I will also delineate the minimal sampling rates for recovering certain useful statistics of non-sparse random signals, along with the compressive sampler designs for approaching such rates.

This line of research is useful in a range of applications as popular as spectrum sensing for cognitive radio, massive MIMO for millimeter 5G communications, statistical analysis of network data, graph signal processing for Big Data analytics, biomedical inference, to list a few.

Relevant graduate research assistanship (GRA) opportunities at George Mason University will be discussed as well.

Profile of the Lecturer:

Dr. Zhi Tian is a Professor in the Electrical and Computer Engineering Department of George Mason University, Fairfax, VA. She joined Mason in January 2015 and is actively recruiting high-quality graduate students. Prior to that, she was on the faculty of Michigan Technological University from 2000 to 2014.  She served as a Program Director at the US National Science Foundation from 2012 to 2014. Her research interests lie in statistical signal processing, wireless communications and wireless sensor networks.  She is an IEEE Fellow. She serves as an IEEE Distinguished Lecturer for both the IEEE Communications Society and the IEEE Vehicular Technology Society. She served as Associate Editor for IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing. She served on many posts at the IEEE, such as General Chair for the 2016 IEEE GlobalSIP Conference, and Vice Chair for the IEEE Signal Processing Society Big Data Special Interest Group.