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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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CS Colloquium: Philipp Kraehenbuehl (UC Berkeley) - The many ways to understand the pixels, and how to teach computers to do so
Wed, Mar 02, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Philipp Krahenbuhl , UC Berkeley
Talk Title: The many ways to understand the pixels, and how to teach computers to do so
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The field of computer vision is arguably seeing one of its most transformative changes in recent history. Convolutional neural networks (CNNs) have revolutionized the field, reaching super-human performance on some long-standing computer vision tasks, such as image classification. The success of these networks is fueled by massive amounts of human-labeled data. However this paradigm does not scale to a deeper and more detailed understanding of images, as it is simply too hard to collect enough human-labeled data. The issue is not that we humans don't understand the image, but we often struggle to convey enough information to successfully supervise a vision system.
In this talk I show how computer vision can go beyond massive human supervision. This involves designing better models that deal with fewer labels, exploiting easier and more intuitive annotations, or coming up with novel optimizations to train deep architectures with far fewer human annotations, or even without any at all. I'll focus on three long standing computer vision problems: semantic segmentation, intrinsic image decomposition and dense semantic correspondences.
Biography: Philipp Krahenbuhl is a postdoctoral researcher at UC Berkeley. He received a B.S. in Computer Science from ETH Zurich in 2009, and a PhD in Computer Science from Stanford University in 2014. Philipp's research interests lie in Computer vision, Machine learning and Computer Graphics. He is particularly interested in deep learning, efficient optimization techniques, and structured output prediction.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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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
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Computer Science General Faculty Meeting
Wed, Mar 02, 2016 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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ASBME GM 8: Tissue Engineering
Wed, Mar 02, 2016 @ 07:00 PM - 08:00 PM
Viterbi School of Engineering Student Organizations
Student Activity
From 3D-printing tissues to creating organs-on-chips, tissue engineering is an up-and-coming field as well as a growing department at USC. If you are interested in learning more, come out to ASBME's 8th general meeting to hear Dr. Megan McCain speak about the recent advances in tissue engineering, what her research seeks to accomplish and what makes this field so exciting to be a part of. As always, food will be provided to members!
Make sure to also check out the Facebook event!
Location: Kaprielian Hall (KAP) - 156
Audiences: Everyone Is Invited
Contact: Associated Students of Biomedical Engineering
