Events for the 5th week of April
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Apr 24, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dennice F. Gayme, Assistant Professor, Johns Hopkins University
Talk Title: Quantifying efficiency and robustness in large-scale networks
Abstract: Dynamical systems coupled over graphs arise in a number of applications from power grids to vehicle networks. These systems are most often characterized in terms of their stability. However, the performance of these networks is also of great importance as it often corresponds to system efficiency and robustness. In this talk, we discuss a broad class of performance measures for first and second order systems whose outputs are defined so that particular performance metrics can be quantified through the input-output H2 norm of the system. We first present results for systems with the same physical interconnection and communication graph structures. We discuss the effect of graph size and interconnection structure for two applications; characterizing transient real power losses in power grids and evaluating long range disorder in vehicular platoons with both relative and absolute velocity feedback. We then extend our results to vehicular networks with arbitrary physical arrangements and communication structures to demonstrate that our proposed suite of performance measures can be adapted to determine the minimum disturbance energy that is required to cause a collision between any two vehicles. Finally, we further explore the effect of graph structure by considering systems with directed communication graphs.
Biography: Dennice F. Gayme is an Assistant Professor and the Carol Croft Linde Faculty Scholar in Mechanical Engineering at the Johns Hopkins University. She earned her B. Eng. & Society from McMaster University in 1997 and an M.S. from the University of California at Berkeley in 1998, both in Mechanical Engineering. She received her Ph.D. in Control and Dynamical Systems in 2010 from the California Institute of Technology, where she was a recipient of the P.E.O. scholar award in 2007 and the James Irvine Foundation Graduate Fellowship in 2003. Her research interests are in modeling, analysis and control for spatially distributed and large-scale networked systems in applications such as wall-bounded turbulent flows, wind farms, power grids and vehicular networks. She was a recipient of the JHU Catalyst Award in 2015, a 2017 ONR Young Investigator award, and an NSF CAREER award in 2017.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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CommNetS seminar
Tue, Apr 25, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christian Grussler, Lund University
Talk Title: Low-Rank Inducing Norms with Optimality Interpretations
Series: CommNetS
Abstract: This talk is on optimization problems which are convex apart from a sparsity/rank constraint. These problems are often found in the context of compressed sensing, linear regression, matrix completion, low-rank approximation and many more. Today, one of the most widely used methods for solving these problems is so-called nuclear norm regularization. Despite the nice probabilistic guarantees of this method, this approach often fails for problems with structural constraints.
In this talk, we will present an alternative by introducing the family of so-called low-rank inducing norms as convexifiers. Each norm is the convex envelope of a unitarily invariant norm plus a rank constraint. Therefore, they have several interesting properties, which will be discussed throughout the talk. They:
i) Give a simple deterministic test if the solution to the convexified problem is a solution to a specific non-convex problem.
ii) Often finds solutions where the nuclear norm fails to give low-rank solutions.
iii) Allow us to analyze the convergence of non-convex proximal splitting algorithms with convex analysis tools.
iv) Provide a more efficient regularization than the traditional scalar multiplication of the nuclear norm.
v) Leads to a different interpretation of the nuclear norm than the one that is traditionally presented.
vi) In particular, all the results can be generalized to so-called atomic norms.
Biography: Christian Grussler is a postdoc at the Department of Automatic Control at Lund University, Sweden. His current research interests include positive systems, model reduction, system identification and low-rank/sparse optimization. He received a Dipl.-Math. techn. degree (Industrial Mathematics) from TU Kaiserslautern, Germany and an M.Sc. degree (Engineering Mathematics) from Lund University in 2011. In 2017, he received a Ph.D. degree from Lund University under the guidance of Anders Rantzer and Pontus Giselsson.
Host: Prof. Mihailo Jovanovic
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Annie Yu
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MHI Emerging Trends Seminar Series
Wed, Apr 26, 2017 @ 10:00 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kai Hwang, Professor, Ming Hsieh Department of Electrical Engineering
Talk Title: Big-Data Analytics for Cloud Computing in Cognitive Applications
Series: Emerging Trends
Abstract: In this talk, Dr. Hwang will address the effective use of big-data analytics on smart clouds, social networks, intelligent robots, and IoT platforms. He will assess machine/deep learning models and available software tools to advance the cognitive service industry represented by Google, Microsoft, Apple, Facebook, Baidu, IBM, Huawei, etc. The ultimate goal is to achieve enhanced agility, mobility, security, and scalability of public clouds, IoT platforms, and social-media networks.
His talk will assess current AI programs and brain projects pursued by high-tech companies, including Google X-Lab, TensorFlow, DeepMind AlphaGo, Nvidia Digits 5 for using GPU in deep learning, IBM neuromorphic computer, and CAS/ICT Camericon project, etc. Some hidden R/D opportunities are revealed for building smart machinesï¼delivery drones, self-driving cars, blockchains, AR/VR gears, etc. Extended cognitive applications will be discussed for 5G health-care, desease detection, emotion control, and social media community services.
Biography: Kai Hwang is a Professor of EE/CS at the Univ. of Southern California. He received the Ph.D. from UC Berkeley. He has published extensively in computer architecture, parallel processing, cloud computing, and network security. His latest two books are entitled: Cloud Computing for Machine Learning and Cognitive Applications (The MIT Press, April 2017) and Big Data Analytics for Cloud/IoT and Cognitive Computing (Wiley, U.K, May 2017).
An IEEE Life Fellow, he received the very-first CFC Outstanding Achievement Award in 2004 and the Lifetime Achievement Award from IEEE Cloud2012 for his pioneering work in parallel computing and distributed systems. Four of his graduated Ph.D. students were elected as IEEE Fellows and one an IBM Fellow. He has delivered four dozens of keynote or distinguished lectures in international Conferences or Research Centers. Dr. Hwang has performed consulting work with IBM, MIT Lincoln Lab, Chinese Academy of Sciences, and INRIA in France. He can be reached via his Email at USC: kaihwang@usc.edu.
Host: Shri Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
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MHI Emerging Trends Seminar Series
Wed, Apr 26, 2017 @ 10:00 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kai Hwang, Professor, Ming Hsieh Department of Electrical Engineering
Talk Title: Big-Data Analytics for Cloud Computing in Cognitive Applications
Series: Emerging Trends
Abstract: In this talk, Dr. Hwang will address the effective use of big-data analytics on smart clouds, social networks, intelligent robots, and IoT platforms. He will assess machine/deep learning models and available software tools to advance the cognitive service industry represented by Google, Microsoft, Apple, Facebook, Baidu, IBM, Huawei, etc. The ultimate goal is to achieve enhanced agility, mobility, security, and scalability of public clouds, IoT platforms, and social-media networks.
His talk will assess current AI programs and brain projects pursued by high-tech companies, including Google X-Lab, TensorFlow, DeepMind AlphaGo, Nvidia Digits 5 for using GPU in deep learning, IBM neuromorphic computer, and CAS/ICT Camericon project, etc. Some hidden R/D opportunities are revealed for building smart machines, delivery drones, self-driving cars, blockchains, AR/VR gears, etc. Extended cognitive applications will be discussed for 5G health-care, disease detection, emotion control, and social media community services.
Biography: Kai Hwang is a Professor of EE/CS at the Univ. of Southern California. He received his Ph.D. from UC Berkeley. He has published extensively in computer architecture, parallel processing, cloud computing, and network security. His latest two books are entitled: Cloud Computing for Machine Learning and Cognitive Applications (The MIT Press, April 2017) and Big Data Analytics for Cloud/IoT and Cognitive Computing (Wiley, U.K, May 2017).
An IEEE Life Fellow, he received the very first CFC Outstanding Achievement Award in 2004 and the Lifetime Achievement Award from IEEE Cloud2012 for his pioneering work in parallel computing and distributed systems. Four of his graduated Ph.D. students were elected as IEEE Fellows and one an IBM Fellow. He has delivered dozens of keynote or distinguished lectures in international Conferences or Research Centers. Dr. Hwang has performed consulting work with IBM, MIT Lincoln Lab, the Chinese Academy of Sciences, and INRIA in France. He can be reached via his Email at USC: kaihwang@usc.edu
Host: Shri Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
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MHI CommNetS seminar
Wed, Apr 26, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Adam Wierman, Caltech
Talk Title: Platforms & Networked Markets: Transparency & Market Power
Series: CommNetS
Abstract: Platforms have emerged as a powerful economic force, driving both traditional markets, like the electricity market, and emerging markets, like the sharing economy. The power of platforms comes from their ability to tame the complexities of networked marketplaces -- marketplaces where there is not a single centralized market, but instead a network of interconnected markets loosely defined by a graph of feasible exchanges. Despite the power and prominence of platforms, the workings of platforms are often guarded secrets, e.g., we know little about how amazon matches buyers and seller and how uber matches drivers and riders. Further, many competing platforms make very different design choices, but little is understood about the impact of these differing choices. In this talk, I will overview recent work that focuses on reverse engineering the design of platforms and understanding the consequences of design choices underlying modern platforms. I will use electricity markets and ridesharing services as motivating examples throughout the talk.
Biography: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he currently serves as Executive Officer. He is also the director of the Information Science and Technology (IST) initiative at Caltech. He is the founding director of the Rigorous Systems Research Group (RSRG) and co-Director of the Social and Information Sciences Laboratory (SISL). His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS. Additionally, he maintains a popular blog called Rigor + Relevance.
Host: Prof. Insoon Yang
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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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