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Events for March 02, 2016
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MHI Distinguished Visitor Talk
Wed, Mar 02, 2016 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. K.J. Ray Liu, University of Maryland
Talk Title: Why Time-Reversal for Future 5G Wireless?
Abstract: Time reversal is a fundamental physical phenomenon that takes advantage of unavoidable but rich multi-paths in radio propagation to create the spatial-temporal resonance effect, the so-called focusing effect. One can image that the larger the transmission power, the more observable multipaths. When the power is fixed, so does the maximum number of observable multipaths. Since radio waves travel at the speed of light, for one to see the multipath profile in detail, it needs high resolution in time, which implies very broad bandwidth in frequency. The larger the bandwidth, the better the time resolution, and therefore the more multipaths can be revealed. In essence, multipaths are naturally existing 'degrees of freedom' ready to be harvested via power and bandwidth. In a real environment, especially indoors, depending on the structure of the buildings, the number of observable multipaths can one observe is around 15-30 significant multipaths with 150 MHz bandwidth - the entire ISM band at 5.8 GHz. Such a large number of degrees of freedom, existing in nature, can be harvested to enable engineering applications. In this talk, we will argue that time-reversal is an ideal platform for future 5G wireless because it realizes the massive multipath effect by using a single antenna and has low complexity as the environment is serving as the computer. It is highly secure and energy efficient, scalable for extreme network densification, and ideal for cloud-based radio networks. It also offers very simply but high resolution for indoor positioning systems, an essential property for Internet of Things applications. Time-reversal meets all the demands one can envision for future 5G wireless!
Biography: Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland, College Park, in 2007, where he is Christine Kim Eminent Professor of Information Technology. He leads the Maryland Signals and Information Group conducting research encompassing broad areas of information and communications technology with recent focus on future wireless technologies, network science, and information forensics and security. Dr. Liu was a recipient of the 2016 IEEE Leon K. Kirchmayer Technical Field Award on graduate teaching and mentoring, IEEE Signal Processing Society 2014 Society Award, IEEE Signal Processing Society 2009 Technical Achievement Award, and various best paper awards. Recognized by Thomson Reuters as a Highly Cited Researcher, he is a Fellow of IEEE and AAAS. Dr. Liu is a member of IEEE Board of Director. He was President of IEEE Signal Processing Society, where he has served as Vice President -“ Publications and the Editor-in-Chief of IEEE Signal Processing Magazine. He also received teaching and research recognitions from University of Maryland including university-level Invention of the Year Award (three times); and college-level Poole and Kent Senior Faculty Teaching Award, Outstanding Faculty Research Award, and Outstanding Faculty Service Award, all from A. James Clark School of Engineering (one award each per year from the entire college).
Host: Prof. Shrikanth Narayanan & Prof. C.-C. Jay Kuo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Mar 02, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gregory Valiant, Stanford University
Talk Title: When your big data seems too small: accurate inferences beyond the empirical distribution
Series: CommNetS
Abstract: We discuss two problems related to the general challenge of making accurate inferences about a complex distribution, in the regime in which the amount of data (i.e the sample size) is too small for the empirical distribution of the samples to be an accurate representation of the underlying distribution. The first problem is the basic task of learning a discrete distribution, given access to independent draws. We show that one can accurately recover the unlabelled vector of probabilities of all domain elements whose true probability is greater than 1/(n log n). Stated differently, one can learn-“up to relabelling-“the portion of the distribution consisting of elements with probability greater than 1/(n log n). This result has several curious implications, including leading to an optimal algorithm for "de-noising" the empirical distribution of the samples, and implying that one can accurately estimate the number of new domain elements that would be seen given a new larger sample, of size up to n * log n. (Extrapolation beyond this sample size is provable information theoretically impossible, without additional assumptions on the distribution.) While these results are applicable generally, we highlight an adaptation of this general approach to some problems in genomics (e.g. quantifying the number of unobserved protein coding variants).
The second problem we consider is the task of accurately estimating the eigenvalues of the covariance matrix of a (high dimensional real-valued) distribution-“the "population spectrum". (These eigenvalues contain basic information about the distribution, including the presence or lack of low-dimensional structure in the distribution and the applicability of many higher-level machine learning and multivariate statistical tools.) As we show, even in the regime where the sample size is linear or sublinear in the dimensionality of the distribution, and hence the eigenvalues and eigenvectors of the empirical covariance matrix are misleading, accurate approximations to the true population spectrum are possible.
This talk is based on three papers, which are joint works with Paul Valiant, with Paul Valiant and James Zou, and with Weihao Kong.
Biography: Greg Valiant joined the Computer Science Department at Stanford as an Assistant Professor in Fall 2013, after completing a postdoc at Microsoft Research, New England. His main research interests are in algorithms, learning, applied probability and statistics; he is also interested in game theory, and has enjoyed working on problems in database theory. Valiant graduated from Harvard with a BA in Math and an MS in Computer Science, and obtained his PhD in Computer Science from UC Berkeley in 2012.
Host: Dr. Mahdi Soltanolkotabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
