SUNMONTUEWEDTHUFRISAT
Events for April 27, 2017
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Measurement and Analysis of Mobile and Social Networks
Thu, Apr 27, 2017 @ 11:00 AM - 12:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Athina Markopoulou, Professor/UC Irvine
Talk Title: Measurement and Analysis of Mobile and Social Networks
Abstract: The majority of Internet traffic today is through mobile devices and social media. Large-scale measurement and analysis of these systems is necessary in order to understand underlying patterns and enable engineering optimizations and new applications. In this talk, I will present highlights of our research in this area.
First, I will discuss online social networks. I will present our "2K+" framework for generating synthetic graphs that resemble online social networks, in terms of joint degree distribution and additional characteristics, such as clustering and node attributes [INFOCOM'13, INFOCOM'15]. This problem was motivated by our prior work on graph sampling [JSAC'11, SIGMETRICS'11, INFOCOM'10] and by popular demand to make the Facebook datasets we collected publicly available.
Second, I will discuss cellular networks. I will present our work on analyzing Call Detail Records (CDRs) in order to characterize human activity in urban environments, with applications to urban ecology [MOBIHOC'15] and ride-sharing [UBICOMP'14, SIGSPATIAL'15-16].
Third, I will present our ongoing work on AntMonitor - a system for monitoring network traffic on mobile devices [SIGCOMM C2BID'15], with applications to privacy leaks detection [MOBICOM Demo'15], crowdsourcing of network performance measurements, and improved wireless access.
Biography: Athina Markopoulou is an Associate Professor in EECS at the University of California, Irvine. She received the Diploma degree in Electrical and Computer Engineering from the National Technical University of Athens, Greece, in 1996, and the Master's and Ph.D. degrees in Electrical Engineering from Stanford University, in 1998 and 2003, respectively. She has held short-term/visiting appointments at SprintLabs (2003), Arista Networks (2005), IT University of Copenhagen (2012-2013), and she co-founded Shoelace Wireless (2012). She has received the NSF CAREER Award (2008), the Henry Samueli School of Engineering Faculty Midcareer Award for Research (2014), and the OCEC Educator Award (2017). She has been an Associate Editor for IEEE/ACM Transactions on Networking (2013-2015), an Associate Editor for ACM CCR (2016), the General Co-Chair for ACM CoNEXT 2016, and the Director of the Networked Systems program at UCI. Her research interests are in the area of networking including mobile systems and mobile data analytics, network measurement, online social networks, network security and privacy, network coding, and multimedia traffic.
Host: Professor Konstantinos Psounis, kpsounis@usc.edu
More Information: Seminar Announcement - Markopoulou 042717.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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PhD Defense
Thu, Apr 27, 2017 @ 01:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Akshay Gadde, University of Southern California
Talk Title: Sampling and Filtering of Signals on Graphs with Applications to Active Learning and Image Processing
Abstract: Processing of signals defined over the nodes of a graph has generated a lot of interest recently. This is due to the emergence of modern application domains such as social networks, web information analysis, sensor networks and machine learning, in which graphs provide a natural representation for the data. Traditional data such as images and videos can also be represented as signals on graphs. A frequency domain representation for graph signals can be obtained using the eigenvectors and eigenvalues of operators which measure the variation in signals taking into account the underlying connectivity in the graph. Spectral filtering can then be defined in this frequency domain. Based on this, we develop a sampling theory for graph signals by answering the following questions: 1. When can we uniquely and stably reconstruct a bandlimited graph signal from its samples on a subset of the nodes? 2. What is the best subset of nodes for sampling a signal so that the resulting bandlimited reconstruction is most stable? 3. How to compute a bandlimited reconstruction efficiently from a subset of samples? The algorithms developed for sampling set selection and reconstruction do not require explicit eigenvalue decomposition of the variation operator and admit efficient, localized implementation. Using graph sampling theory, we propose effective graph based active semi-supervised learning techniques. We also give a probabilistic interpretation for the proposed techniques. Based on this interpretation, we generalize the framework of active learning on graphs using Bayesian methods to give an adaptive sampling method. Additionally, we study the application graph spectral filtering in image processing by representing the image as a graph, where the nodes correspond to the pixels and edge weights capture the similarity between them given by the coefficients of the bilateral filter. We show that the bilateral filter is a low pass graph spectral filter with linearly decaying spectral response. We then generalize the bilateral filter by defining filters on the above graph with different spectral responses depending on the application. We also consider the problem of constructing a sparse graph from the given data efficiently, which can be used in graph based learning and fast image adaptive filtering.
Biography: Akshay Gadde received his Bachelor of Technology degree in Electrical Engineering from Indian Institute of Technology (IIT), Kharagpur, India in 2011. He has been working towards a Ph.D. in Electrical Engineering at the University of Southern California (USC), Los Angeles since 2011. His work (with Prof. Antonio Ortega and Aamir Anis) won the Best Student Paper Award at ICASSP 2014. His research interests include graph signal processing and machine learning with applications to multimedia data processing and compression.
Host: Dr. Antonio Ortega
More Information: Gadde Seminar Announcement.png
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
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Computer Architectures for Deep Learning Applications
Thu, Apr 27, 2017 @ 03:30 PM - 05:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Brooks, Harvard University
Talk Title: Computer Architectures for Deep Learning Applications
Abstract: Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this talk describes the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling. The talk will then consider novel computer architectures that can improve the performance and efficiency of deep learning workloads.
Biography: David Brooks is the Haley Family Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Prior to joining Harvard, he was a research staff member at IBM T.J. Watson Research Center. Prof. Brooks received his BS in Electrical Engineering at the University of Southern California and MA and PhD degrees in Electrical Engineering at Princeton University. His research interests include resilient and power-efficient computer hardware and software design for high-performance and embedded systems. Prof. Brooks is a Fellow of the IEEE and has received several honors and awards including the ACM Maurice Wilkes Award, ISCA Influential Paper Award, NSF CAREER award, IBM Faculty Partnership Award, and DARPA Young Faculty Award.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
