Arizona State University Network Science Seminar Series

Semesters

  • Spring 2017
  • Fall 2016
  • Spring 2016
  • Fall 2015
  • Spring 2015
  • Fall 2014
  • Spring 2014
  • Fall 2013
  • Spring 2013
  • Arizona State University Network Science Seminar Series

    Spring 2017 Seminar List

    Title Speaker Time Location
    Subspace Detection with Applications Louis Scharf (Colorado State University) 1:30 p.m., Jan 26th, 2017 GWC 487
    An Informational Perspective on Uncertainty in Control Gireeja Ranade (Microsoft Research, Redmond) 1:30 p.m., Feb 27th, 2017 GWC 409
    Orthogonal precoding for sidelobe suppression in DFT-based systems using block reflectors Vaughan Clarkson (University of Queensland) 1:30 p.m., Mar 3rd, 2017 GWC 487
    On addressing uncertainty and high-dimensionality in optimization and variational inequality problems: self-tuned stepsizes, and randomized block coordinate schemes Farzad Yousefian (Oklahoma State University) 1:30 p.m., Mar 17th, 2017 GWC 487
    Network interference cancelation Olav Tirkkonen (Aalto University, Finland) 1:30 p.m., April 6th, 2017 GWC 487
    Fog Computing and Networking: A New Paradigm for 5G and IoT Services T. Russell Hsing (National Chiao Tung University, Taiwan) 2:00 p.m., May 23rd, 2017 GWC 487

    Fall 2016 Seminar List

    Title Speaker Time Location
    Delay-Optimal Scheduling for Data Center Networks and Input-Queued Switches in Heavy Traffic Siva Theja Maguluri (IBM) 1:30 p.m., Oct 28th, 2016 GWC 487
    The High-Dimensional Limit of Stochastic Iterative Methods for Convex and Nonconvex Optimization: Dynamics and Phase Transitions Yue M. Lu (Harvard University) 10:30 a.m., Sept 27th, 2016 GWC 487

    Spring 2016 Seminar List

    Title Speaker Time Location
    A Unified Framework for Large-Scale Block-Structured Optimization Mingyi Hong (Iowa State University) 1:30 p.m., February 26th, 2016 GWC 487
    Stochastic and Information-theoretic Approaches to Analysis and Storage of Biological Data Farzad Farnoud (California Institute of Technology) 1:30 p.m., March 11th, 2016 GWC 487
    Minimizing Latency in Cloud Based Systems: Replication Over Parallel Servers Yin Sun (Ohio State University) 3:30 p.m., April 7th, 2016 GWC 487
    SGD and Randomized Projections Methods for Linear Systems Deanna Needell (Claremont McKenna College) 1:30 p.m., April 8th, 2016 GWC 487
    Modeling and Optimizing Complex Dynamic Transportation Systems: A State-space-time Network-based Framework Xuesong Zhou (Arizona State University) 1:30 p.m., April 15th, 2016 GWC 487

    Fall 2015 Seminar List

    Title Speaker Time Location
    Privacy against inference attacks: From Theory to Practice Nadia Fawaz (Technicolor Research Center in Los Altos, CA) 10:30 a.m., August 21st, 2015 GWC 487
    Cloud Storage Space vs. Download Time for Large Files Emina Soljanin (Bell Labs, Murray Hill, NJ) 1:30 p.m., ​September 1​1th, 201​5 GWC 487
    Compressed Sensing and High-Resolution Image Inversion: Cautionary Notes Ali Pezeshki (Colorado State University) 1:30 p.m., October 9th, 201​5​ GWC 487
    Distributed Optimization and Learning in Networks Angelia Nedich (University of Illinois at Urbana-Champaign) 1:30 p.m., October 16th, 2015 GWC 487
    Data-driven Information Divergence Measures Visar Berisha (Arizona State University) 1:30 p.m., October 23rd, 2015 GWC 487
    Enhancing STEM Education Through Robotics Outreach - A Pipeline Approach Tanja Karp (Texas Tech University) 1:30 p.m., November 23rd, 2015 GWC 487
    Using Software-Defined Networking to Radically Simplify and Harden Enterprise Networks Bryan Larish (National Security Agency) 11:00 a.m., November 30th, 2015 GWC 487

    Spring 2015 Seminar List

    Title Speaker Time Location
    Mean Field Games: An Approach to Understanding Resource Sharing Systems Srinivas Shakkottai (Texas A&M University) 2:00 p.m. March 17th, 2015 GWC 487
    Green Multi-Homing Video Transmission in Wireless Heterogeneous Networks Weihua Zhuang (University of Waterloo) 10:00 a.m, March 18th, 2015 GWC 487
    ITLinQ: A New Approach for Spectrum Sharing in Device-to-Device Communication Systems Salman Avestimehr (University of Southern California) 1:30 p.m., April 10th, 2015 GWC 487
    Signal Processing and Communication Challenges for the Internet of Energy Anna Scaglione(Arizona State University) 1:30 p.m., May 1st, 2015 GWC 487

    Fall 2014 Seminar List

    Title Speaker Time Location
    Social Group Utility Maximization for Mobile Social Networks Junshan Zhang (Arizona State University) 1:30 p.m. September 5th, 2014 GWC 487
    Estimation and Registration on Graphs Douglas Cochran (Arizona State University) 1:30 p.m. September 12th, 2014 GWC 487
    Dynamic Mathematical Modeling of Information Diffusion in Online Social Networks Feng Wang (Arizona State University) 1:30 p.m. September 19th, 2014 GWC 487
    A Boundedly Rational User Equilibrium Model for the Traffic Assignment Problem Yingyan Lou (Arizona State University) 1:30 p.m. September 26th, 2014 ISTB1-227
    Design and stability of load-side frequency control Steven H. Low (California Institute of Technology) 1:30 p.m. October 3rd, 2014 GWC 487
    Agnostic Protocol Translation for Cross-Domain Information sharing Chen Liu (UtopiaCompression Corporation) 3:00 p.m. October 7th, 2014 GWC 487
    Joint Radar-Communications Performance Inner Bounds: Data versus Estimation Information Rates Daniel Bliss (Arizona State University) 1:30 p.m. October 17th, 2014 GWC 487
    A New Geometric Approach to Topic Modeling and Discovery Prakash Ishwar(Boston University) 1:30 p.m. October 31st, 2014 GWC 487
    When SDN Meets Security: New Opportunities and Challenges Guofei Gu (Texas A&M University) 2:30 p.m. November 6th, 2014 GWC 487
    What is the Power of Groups? Ram Rajagopal(Stanford University) 1:30 p.m. November 21st, 2014 GWC 487
    The Cost of Free Spectrum Michael Honig(Northwestern University) 10:30 a.m. December 17th, 2014 GWC 487

    Spring 2014 Seminar List

    Title Speaker Time Location
    Maximum pressure policies for stochastic processing networks Jim Dai (Cornell University) 1:30 p.m. January 9th, 2014 GWC 487
    Scheduling Real-Time Traffic in Wireless Ad Hoc Networks Lei Ying (Arizona State University) 1:30 p.m. January 30th, 2014 GWC 487
    Evaluating Mobile Video and Mobile Applications Prasant Mohapatra (University of California at Davis) 11:00 a.m. February 14th, 2014 GWC 487
    Coherence in its many guises Louis Scharf (Colorado State University) 1:30 p.m., February 27th, 2014 GWC 487
    Exact Asymptotics and Moderate Deviations in Channel Coding Aaron B. Wagner (Cornell University) 11:00 a.m., March 27th, 2014 GWC 487
    Wind Aggregation via Risky Power Markets Yue Zhao (Princeton University and Stanford University) 1:30 p.m., April 10th, 2014 GWC 487
    Communication, Control and Sensing: Connecting Communication over Channels with State with the Witsenhausen Counterexample for Distributed Control Urbashi Mitra (University of Southern California) 10:45 a.m., April 16th, 2014 GWC 487
    Online Learning in Communication Networks Bhaskar Krishnamachari (University of Southern California) 1:30 p.m., May 8th, 2014 GWC 487

    Fall 2013 Seminar List

    Title Speaker Time Location
    Asymptotic Analyses of Multiantenna Systems in Spatially Distributed Networks Siddhartan Govindasamy (Franklin W. Olin College of Engineering) 12:00 p.m. September 12th, 2013 GWC 487
    Wireless Body Area Network and Remote Healthcare System Xuemin (Sherman) Shen (University of Waterloo) 1:30 p.m. October 3rd, 2013 GWC 487
    The Sketched SVD and Applications in Structural Health Monitoring Michael B. Wakin (Colorado School of Mines) 1:30 p.m. October 18th, 2013 GWC 487
    Cyber-Physical Security of the Smart Grid Deepa Kundur (University of Toronto) 1:00 p.m. October 31st, 2013 GWC 487
    Randomized Load Balancing in Large Processor Sharing Systems Ravi R. Mazumdar (University of Waterloo) 1:30 p.m. November 7th, 2013 GWC 487
    Searching And Bargaining With Middlemen Vijay G. Subramanian (Northwestern University) 1:30 p.m. December 5th, 2013 GWC 487

    Spring 2013 Seminar List

    Title Speaker Time Location
    Resource Allocation Algorithms for Cloud Computing Siva Theja Maguluri (University of Illinois at Urbana-Champaign) 3:00 p.m. January 11th, 2013 GWC 487
    A Geometric Method For Networks Lizhong Zheng (Massachusetts Institute of Technology) 11:00 a.m. January 23rd, 2013 GWC 487
    Sensor Management via Riemannian Geometry Part I – Basic concepts of information geometry Douglas Cochran (Arizona State University) 1:30 p.m. February 7th, 2013 GWC 487
    Low-Complexity Scheduling Policies for Achieving Throughput and Delay Optimality in Multi-channel (OFDM) Downlink Systems Ness Shroff (The Ohio State University) 1:30 p.m. February 28th, 2013 GWC 487
    Games, Privacy and Distributed Inference for the Smart Grid H. Vincent Poor (Princeton University) 1:30 p.m. March 15th, 2013 GWC 487
    Spectrum Auctions in The Air: Real-Time Wireless Spectrum Trading Alhussein Abouzeid (Rensselaer Polytechnic Institute) 1:30 p.m. March 21st, 2013 GWC 487
    Non-Uniform OFDM Pilot Power Distributions A Lee Swindlehurst (University of California, Irvine) 1:30 p.m. March 28th, 2013 GWC 487
    Hummingbird: Privacy at the time of Twitter Gene Tsudik (University of California, Irvine) 2:00 p.m. April 5th, 2013 USE 104
    Visual Information Acquisition, Noisy Search, and Active Hypothesis Testing Tara Javidi (University of California, San Diego) 1:30 p.m. April 18th, 2013 GWC 487
    Secure Wireless File Distribution Anthony Ephremides (University of Maryland) 1:30 p.m. April 25th, 2013 GWC 487
    Block Regularized Lasso for Multivariate Multi-Response Linear Regression Yingbin Liang (Syracuse University) 1:30 p.m. May 2nd, 2013 GWC 487
    How To Clean a Slate John Day (Boston University) 1:00 p.m. May 13th, 2013 GWC 487
    Coding and Information-Theoretic Aspects of Coordination in Networks Joerg Kliewer (New Mexico State University) 1:00 p.m. May 21st, 2013 GWC 487

    Asymptotic Analyses of Multiantenna Systems in Spatially Distributed Networks

    Speaker Siddhartan Govindasamy (Franklin W. Olin College of Engineering)
    Date 12:00 p.m. September 12th, 2013
    Location GWC 487
    Short Bio
    Dr. Govindasamy was born in Malaysia where he completed his high school education. He obtained B.S. and M-Eng degrees from the Massachusetts Institute of Technology (MIT) in 1999 and 2000 respectively. At MIT he participated in the VI-A internship program at Qualcomm Inc. in San Diego, CA where he developed signal processing algorithms for mobile telephony. From 2000 to 2003, he was a DSP engineer and then Senior DSP engineer at Aware Inc. in Bedford, MA where he designed and developed broadband modem technology. He returned to MIT in 2003, and obtained a Ph. D. in wireless communications in 2008. His thesis research was on ad-hoc wireless communications using multi-antenna systems. In the course of his Ph. D. he visited the Indian Institute of Science in Bangalore India as part of the MIT-India program in the summer of 2006. He has been a member of the faculty at Olin College since August 2008.
    Abstract
    As wireless networks become increasingly crowded, and with reducing hardware costs, large antenna arrays are becoming a promising technology to mitigate interference in dense networks. As a result technologies such as massive multiple-input multiple-output (MIMO) systems have been proposed. In addition to improving performance, the large numbers of degrees of freedom in such systems enables the use of asymptotic analyses which can help yield insight into wireless networks which are difficult to analyze in the finite domain. In this talk, results on the analysis of wireless networks with users at correlated spatial positions, which have proven difficult to analyze in the past, will be presented. Approximations, which are precise in a certain asymptotic sense, are derived for networks with hard-cores around users, uplinks of cellular networks with time-division-multiple access and frequency reuse patterns, and networks with spatially clustered users. These results were derived by combining infinite random matrix theory and stochastic geometry. Monte Carlo simulations used to validate these results indicate that the approximations are accurate even when the number of antennas is moderately large. Besides providing simple expressions for achievable data rates, these results can also be used to optimize system parameters such as frequency reuse factors.

    Wireless Body Area Network and Remote Healthcare System

    Speaker Xuemin (Sherman) Shen (University of Waterloo)
    Date 1:30 p.m. Octobor 3rd, 2013
    Location GWC 487
    Short Bio
    Healthcare is becoming a serious social issue due to the increasing aging population, doctor shortages, and rising costs. Remote Healthcare System (RHS) based on wireless body area network (WBAN) has attracted significant attention recently since it not only cares for onsite patients, but also extends these benefits to anyone demanding healthcare beyond medical facilities. In addition, it can act as a dedicated virtual doctor and nurse, providing healthcare anywhere, around-the-clock with low cost. In other words, RHS can provide enhanced health care services with low cost. This talk will focus on the WBAN in terms of emerging technologies, challenge design issues, and its application to Remote Healthcare System.
    Abstract
    Xuemin (Sherman) Shen is a Professor and University Research Chair, Department of Electrical and Computer Engineering, University of Waterloo, Canada. Dr. Shen’s research focuses on resource management in interconnected wireless/wired networks, wireless network security, wireless body area networks and vehicular ad hoc and sensor networks. He is the Editor-in-Chief of IEEE Network, and IET Communications. He served as the Technical Program Committee Chair/Co-Chair for IEEE VTC’10, the Symposia Chair for IEEE ICC’10, the Tutorial Chair for IEEE ICC’08, the Technical Program Committee Chair for IEEE Globecom'07, the Founding Chair for IEEE Communications Society Technical Committee on P2P Communications and Networking. He also served as a Founding Area Editor for IEEE Transactions on Wireless Communications; Associate Editor for IEEE Transactions on Vehicular Technology; etc., and the Guest Editor for IEEE JSAC, IEEE Wireless Communications, and IEEE Communications Magazine. Dr. Shen received the Excellent Graduate Supervision Award in 2006, and the Outstanding Performance Award in 2004, 2007, and 2010 from the University of Waterloo, the Premier’s Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an IEEE Fellow, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, and a Distinguished Lecturer of IEEE Vehicular Technology Society and Communications Society.

    The Sketched SVD and Applications in Structural Health Monitoring

    Speaker Michael B. Wakin (Colorado School of Mines)
    Date 1:30 p.m. October 18th, 2013
    Location GWC 487
    Short Bio
    Michael B. Wakin received the B.S. degree in electrical engineering and the B.A. degree in mathematics in 2000 (summa cum laude), the M.S. degree in electrical engineering in 2002, and the Ph.D. degree in electrical engineering in 2007, all from Rice University. He was an NSF Mathematical Sciences Postdoctoral Research Fellow at the California Institute of Technology from 2006-2007 and an Assistant Professor at the University of Michigan in Ann Arbor from 2007-2008. He is now an Associate Professor in the Department of Electrical Engineering and Computer Science at the Colorado School of Mines. His research interests include sparse, geometric, and manifold-based models for signal and image processing, approximation, compression, compressive sensing, and dimensionality reduction. In 2007, Dr. Wakin shared the Hershel M. Rich Invention Award from Rice University for the design of a single-pixel camera based on compressive sensing; in 2008, Dr. Wakin received the DARPA Young Faculty Award for his research in compressive multi-signal processing for environments such as sensor and camera networks; and in 2012, Dr. Wakin received the NSF CAREER Award for research into dimensionality reduction techniques for structured data sets.
    Abstract
    We present a simple technique for estimating parts of the Singular Value Decomposition (SVD) of a data matrix from a small randomly compressed "sketch" of that matrix. In sensor network settings--where each column of the data matrix comes from a separate sensor--the sketch can be assembled using operations local to each sensor. As an application of this work, we consider the problem of Structural Health Monitoring (SHM). SHM systems are critical for monitoring aging infrastructure (such as buildings or bridges) in a cost-effective manner. Such systems typically involve collections of battery-operated wireless sensors that sample vibration data over time. After the data is transmitted to a central node, modal analysis can be used to detect damage in the structure. We propose and study three frameworks for Compressive Sensing(CS) in SHM systems; these methods are intended to minimize power consumption by allowing the data to be sampled and/or transmitted more efficiently. At the central node, all of these frameworks involve a very simple technique for estimating the structure's mode shapes without requiring a traditional CS reconstruction of the vibration signals; all that is needed is to compute a simple SVD. We support our proposed techniques theoretically and using simulations based on synthetic and real data. This project is joint work with Anna Gilbert and Jae Young Park.

    Cyber-Physical Security of the Smart Grid

    Speaker Deepa Kundur (University of Toronto)
    Date 1:00 p.m. October 31st, 2013
    Location GWC 487
    Short Bio

    Deepa Kundur is a Professor at The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. A native of Toronto, Canada, she received the B.A.Sc., M.A.Sc., and Ph.D. degrees all in Electrical and Computer Engineering in 1993, 1995, and 1999, respectively, from the University of Toronto. From September 1999 to December 2002 she was an Assistant Professor in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto and returned in September 2012 to hold the title of Professor. From January 2003 to December 2012 she was a faculty member in Electrical & Computer Engineering at Texas A&M University.

    Dr. Kundur’s research interests include cybersecurity of the electric smart grid, cyber-physical system theory, security and privacy of social and sensor networks, multimedia security, and computer forensics. She is an appointed member of the NERC Smart Grid Task Force and was recently the Technical Program Co-Chair for the 2012 IEEE International Workshop on Information Forensics and Security. She has been on several editorial boards and is the recipient of numerous teaching awards at both the University of Toronto and Texas A&M University. Her research has received best paper recognitions at the 2008 INFOCOM Workshop on Mission Critical Networks, the 2011 Cyber Security and Information Intelligence Research Workshop, and the 2012 IEEE Canadian Conference on Electrical and Computer Engineering.
    Abstract
    The scale and complexity of the smart grid, along with its increased connectivity and automation, make the task of its cyber protection challenging. Recently, smart grid researchers and standards bodies have begun to develop technological requirements and potential solutions for protecting cyber infrastructure. However, grid protection remains daunting to asset owners because of resources limitations. Important questions arise when identifying priorities for design and protection: Which cyber components, if compromised, can lead to significant power delivery disruption? What grid topologies are inherently robust to classes of cyber attack? Is the additional information available through advanced information technology worth the increased security risk? We assert that a key research challenge in addressing these fundamental questions lies in the effective understanding of the cyber-physical synergy of the smart grid. This gives rise to the problem of cyber-physical system security. In this talk, we introduce this emerging problem in the context of the smart grid and present dynamical systems-based frameworks for modeling cyber-physical interactions. We demonstrate how our approaches enable the identification of emergent vulnerabilities and the evaluation of the relative impacts of communication failure on the flow of electricity. The overall framework facilitates more comprehensive risk analysis and guidelines for resilient smart grid development.

    Randomized Load Balancing in Large Processor Sharing Systems

    Speaker Ravi R. Mazumdar (University of Waterloo)
    Date 1:30 p.m. November 7th, 2013
    Location GWC 487
    Short Bio
    The speaker was educated at the Indian Institute of Technology, Bombay (B.Tech, 1977), Imperial College, London (MSc, DIC, 1978) and obtained his PhD under A. V. Balakrishnan at UCLA in 1983. He is currently a University Research Chair Professor in the Dept. of ECE at the University of Waterloo, Ont., Canada where he has been since September 2004. Prior to this he was Professor of ECE at Purdue University, West Lafayette, USA. He is a Fellow of the IEEE and the Royal Statistical Society. He is a recipient of the INFOCOM 2006 Best Paper Award and was runner-up for the Best Paper Award at INFOCOM 1998. His research interests are in modeling, control, and performance analysis of both wireline and wireless networks, and in applied probability and stochastic analysis with applications to queueing, filtering, and optimization.
    Abstract

    Processor sharing models occur in a wide variety of situations. They are good models for bandwidth sharing as well as being solutions to NUM for logarithmic utilities. In addition they possess the desirable stochastic property of the stationary distribution being insensitive to the service time distribution. In this talk I will discuss new advances in understanding and characterizing the behavior of randomized routing to PS servers that are heterogeneous in terms of their server capacities.

    In particular, starting with the identical server case we will first discuss the so-called Power-of-two rule where by a combination of routing to the least occupied server amongst two randomly chosen servers results in a very low server occupancy and a so-called propagation of chaos or asymptotic independence. Using these insights we analyze the case of heterogeneous servers where the server capacity can be one of M. We provide a complete characterization of the stationary distribution and prove that the limiting system is insensitive. We then consider a modified criterion based on routing to the server with lower Lagrange costs. We compare these dynamic routing strategies with an optimal static state independent scheme. We show that the dynamic schemes are much better in terms of average delay with the Lagrange cost based being the best.

    The techniques are based on a mean field analysis and an ansatz based on propagation of chaos.

    Joint work with Arpan Mukhopadhyay (Waterloo).

    Searching And Bargaining With Middlemen

    Speaker Vijay G. Subramanian (Northwestern University)
    Date 1:30 p.m. December 5th, 2013
    Location GWC 487
    Short Bio
    Vijay Subramaniam received the B.Tech. degree in Electronics Engineering from IIT Madras in 1993. Subsequently, he obtained M.Sc. (Engg.) degree in Electrical Communication Engineering from IISc Bangalore in 1995 and Ph.D. in Electrical Engineering from University of Illinois at Urbana-Champaign. He worked in the research arm of the Networks Business Sector of Motorola in Arlington Heights, Illinois, USA until May 2006. In May 2006, he moved to the Hamilton Institute of the National University of Ireland, Maynooth as a Research Fellow. During Summer 2010, he was a visiting reseacher at LIDS MIT. From Nov 2010 to Oct 2011, he was a Senior Research Associate in the EECS Department at Northwestern University. Currently, he is a Research Assistant Faculty in the EECS Department at Northwestern University. His interests are in social networks, network economics, random graphs, communication networks, information theory and applied probability.
    Abstract
    We study decentralized markets which include middlemen, producers and consumers connected via a trading network. We develop a model for trade in such settings based on non-cooperative bargaining with search frictions. Our goal is to investigate how the structure of the trading network and the role of middlemen influence the market's efficiency and fairness. To this end, we introduce the concept of limit stationary equilibrium which characterizes the trading patterns that emerge in a general trading network with a large population of agents. We use this concept to analyze how competition among middlemen is influenced by the network structure, how endogenous delay emerges in trade and how surplus is shared between producers and consumers.

    Resource Allocation Algorithms for Cloud Computing

    Speaker Siva Theja Maguluri (University of Illinois at Urbana-Champaign)
    Date 3:00 p.m. January 11th, 2013
    Location GWC 487
    Short Bio
    Siva Theja Maguluri received his B.Tech from the Department of Electrical Engineering, Indian Institute of Technology Madras in 2008. He received his MS from University of Illinois Urbana Champaign and is currently a PhD candidate at Coordinated Science Lab, UIUC. His Research Interests include Cloud Computing, Resource Allocation Algorithms, Wireless Networks and Game Theory.
    Abstract
    Cloud computing is emerging as an important platform for business, personal and mobile computing applications. We study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. Job sizes (durations) are also modeled as random variables, with possibly unbounded support. These jobs need to be scheduled non preemptively on servers. The jobs are first routed to one of the servers when they arrive and are queued at the servers. Each server then chooses a set of jobs from its queues so that it has enough resources to serve all of them simultaneously. We present a load balancing and scheduling algorithm that is throughput optimal and delay optimal in the heavy traffic limit.

    A Geometric Method For Networks

    Speaker Lizhong Zheng (Massachusetts Institute of Technology)
    Date 11:00 a.m. January 23rd, 2013
    Location GWC 487
    Short Bio
    Lizhong Zheng received the B.S and M.S. degrees, in 1994 and 1997 respectively, from the Department of Electronic Engineering, Tsinghua University, China, , and the Ph.D. degree, in 2002, from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Since 2002, he has been working in the Department of Electrical Engineering and Computer Sciences, where he is currently an associate professor. His research interests include information theory, wireless communications and wireless networks. He received Eli Jury award from UC Berkeley in 2002, IEEE Information Theory Society Paper Award in 2003, and NSF CAREER award in 2004, and the AFOSR Young Investigator Award in 2007.
    Abstract
    In this talk, we introduce a few recent results on using information geometry to network communication problems and some other general information exchange problems. The focus of the talk is to explain why a geometric tool is needed, and missed, in the current development of information theory. We demonstrate that with only some crude approximation, one can already gain from such geometric approaches some insights that are not available from the classical methods. The specific examples we will discuss include some progresses on general broadcast channels, and a new network model.

    Sensor Management via Riemannian Geometry Part I – Basic concepts of information geometry

    Speaker Douglas Cochran (Arizona State University)
    Date 1:30 p.m. February 7th, 2013
    Location GWC 487
    Short Bio
    Douglas Cochran joined the the ASU faculty in 1989. Before coming to ASU, he was a senior scientist at BBN Laboratories. Professor Cochran has served as program manager for mathematics in the U.S. Defense Advanced Research Projects Agency, as a consultant for the Australian Defense Science and Technology Organisation, as associate editor of the IEEE Transactions on Signal Processing, and as general co-chair of the 1999 IEEE International Conference for Acoustics, Speech and Signal Processing and the 1997 U.S.-Australia Workshop on Defense Signal Processing.
    Abstract
    In estimation of scenario parameters from sensor data, the Fisher information induces a Riemannian metric on the manifold of parameters. If the collection of sensors is reconfigured, this metric changes. In this way, sensor configurations are identified with Riemannian metrics on the parameter manifold. The collection of all Riemannian metrics on a manifold forms a (weak) Riemannian manifold, and smooth changes in configuration of the sensor suite manifests as a smooth curve in this space. The goal of this two-part presentation is to examine the idea of sensor management via navigation along geodesics in a submanifold of this space corresponding to physically viable sensor configurations; i.e., curves that optimize an energy integral in the submanifold. This first part will provide an introduction to information geometry, including some general concepts but focusing particularly on the ideas essential in the research to be presented in Part 2 later in the seminar series.

    Low-Complexity Scheduling Policies for Achieving Throughput and Delay Optimality in Multi-channel (OFDM) Downlink Systems

    Speaker Ness B. Shroff (The Ohio State University)
    Date 1:30 p.m. February 28th, 2013
    Location GWC 487
    Short Bio
    Ness Shroff received his Ph.D. degree in Electrical Engineering from Columbia University in 1994. He joined Purdue university immediately thereafter as an Assistant Professor in the school of ECE. At Purdue, he became Full Professor of ECE in 2003 and director of CWSA in 2004, a university-wide center on wireless systems and applications. In July 2007, he joined The Ohio State University, where he holds the Ohio Eminent Scholar endowed chair professorship in Networking and Communications, in the departments of ECE and CSE. From 2009-2012, he served as a Guest Chaired professor of Wireless Communications at Tsinghua University, Beijing, China, and currently holds an honorary Guest professor at Shanghai Jiaotong University in China. His research interests span the areas of communication, social, and cyberphysical networks. He is especially interested in fundamental problems in the design, control, performance, pricing, and security of these networks. Dr. Shroff is a past editor for IEEE/ACM Trans. on Networking and the IEEE Communication Letters. He currently serves on the editorial board of the Computer Networks Journal, IEEE Network Magazine, and the Networking Science journal. He has chaired various conferences and workshops, and co-organized workshops for the NSF to chart the future of communication networks. Dr. Shroff is a Fellow of the IEEE and an NSF CAREER awardee. He has received numerous best paper awards for his research, e.g., at IEEE INFOCOM 2008, IEEE INFOCOM 2006, Journal of Communication and Networking 2005, Computer Networks 2003 (also one of his papers was a runner-up at IEEE INFOCOM 2005), and also student best paper awards (from all papers whose first author is a student) at IEEE WiOPT 2012 and IEEE IWQoS 2006.
    Abstract

    The dramatic increases in demands from multimedia applications have put an enormous strain on the current cellular system infrastructure. This has resulted in significant research and development efforts on 4G multi-channel wireless cellular systems (e.g., LTE and WiMax) that target new ways to achieve higher data rates, lower latencies, and a much better user experience. An important requirement for achieving these goals is to design efficient scheduling policies that can simultaneously provide high throughput and low delay. In these multi-channel systems, such as OFDM, the Transmission Time Interval (TTI), within which the scheduling decisions need to be made, is typically on the order of a few milliseconds. On the other hand, there are hundreds of orthogonal channels that can be allocated to different users. Hence, many decisions have to be made within a short scheduling cycle, which means that it is critical that scheduling policies must be of low complexity.

    In this talk, we will present a unifying framework for designing low-complexity scheduling policies in the downlink of multi-channel (e.g., OFDM-based) wireless networks that can provide optimal performance in terms of both throughput and delay. We first develop new easy-to verify sufficient conditions for rate-function delay-optimality in the many-channel many-user asymptotic regime, and for throughput-optimality in general (non-asymptotic) settings. The sufficient conditions enable us to prove rate-function delay-optimality for a class of Oldest Packets First (OPF) policies and throughput optimality for a large class of Maximum Weight in the Fluid limit (MWF) policies. While a recently developed scheduling policy is both throughput-optimal and rate-function delay-optimal, it has a very high complexity of O(n^5), where n is the number of channels or users, rendering it impractical. By intelligently combining policies from the classes of OPF policies and MWF policies, we design hybrid policies that have a low complexity of O(n^{2.5} log n), and are yet both throughput and rate-function delay optimal. We further develop two simpler greedy policies that are throughput-optimal and have a near-optimal rate-function. We show through simulations that these simpler mechanisms have near-optimal value of rate-function in various scenarios. Finally, we propose a class of throughput-optimal policies with even lower complexity that allow an explicit trade-off between complexity and delay performance.

    Games, Privacy and Distributed Inference for the Smart Grid

    Speaker H. Vincent Poor (Princeton University)
    Date 1:30 p.m. March 15th, 2013
    Location GWC 487
    Short Bio
    H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University, where he is also the Dean of Engineering and Applied Science. His research interests are primarily in the areas of stochastic analysis, statistical signal processing, and information theory, and their applications in various fields, including wireless communications, social networks and smart grid. Dr. Poor is a Fellow of the IEEE, and is a member of the National Academy of Engineering, the National Academy of Sciences, and the Royal Academy of Engineering of the UK. Recent recognition of his work includes the 2011 IEEE Eric E. Sumner Award, and honorary doctorates from Aalborg University, the Hong Kong University of Science and Technology, and the University of Edinburgh.
    Abstract
    Smart grid involves the imposition of an advanced cyber layer atop the physical layer of the electricity grid in order to improve the efficiency and lower the cost of power use and distribution, and to allow for the effective integration of variable energy sources and storage modes into the grid. This cyber-physical setting motivates the application of many techniques from the information and systems sciences to problems arising in the electricity grid, and considerable research effort has been devoted to such application in recent years. This talk will describe recent work on three aspects of this problem: applications of game theory to smart grid design; characterization of the fundamental tradeoff between privacy and utility of information sources arising in the grid; and distributed inferential algorithms that are suitable for the topological constraints imposed by the structure of the grid.

    Spectrum Auctions in The Air: Real-Time Wireless Spectrum Trading

    Speaker Alhussein Abouzeid (Rensselaer Polytechnic Institute)
    Date 1:30 p.m. March 21st, 2013
    Location GWC 487
    Short Bio
    Alhussein A. Abouzeid received the B.S. degree with honors from Cairo University, Cairo, Egypt in 1993, and the M.S. and Ph.D. degrees from University of Washington, Seattle, WA in 1999 and 2001, respectively, all in electrical engineering. From 1993 to 1994, he was with the Information Technology Institute, Information and Decision Support Center, The Cabinet of Egypt. From 1994 until 1997, he served as a Project Manager in the Middle East Regional Office of Alcatel telecom. He held visiting appointments with Allied Signal -now Honeywell- Redmond, WA, in 1999 and Hughes Research Labs, Malibu, CA, in 2000. In 2001 he joined Rensselaer Polytechnic Institute, Troy, NY, where he is currently an associate professor in the Electrical, Computer and Systems Engineering Department. From 2008 to 2010, he served as a Program Director, Computer & Information Science & Engineering Directorate, Computer & Network Systems Division, where he was responsible for the Networking Technologies and Systems program, and he co-founded the Enhancing Access to Radio Spectrum (EARS) program. He is co-directing Wi.Fi.US, a Virtual Institute for Wireless Research between US and Finland, which is collaboration between 20 US and Finnish institutions. He serves/served on various conferences organization committees and editorial boards of various journals including Elsevier Computer Networks, IEEE Transaction on Wireless Communications and IEEE Wireless Communications Magazine.
    Abstract
    Recent advances in radio technologies, coupled with recent developments in radio frequency regulatory policies, promise to spur innovations in the design of network communication protocols for improved spectrum utilization. In this talk, we present spectrum auctions as an effective mechanism for real-time spectrum sharing. We first review key components and notable applications of auctions theory, including billion-dollar examples such as Internet advertisement and spectrum licensing. We then present challenges and opportunities in utilizing auctions theory for dynamic spectrum access in wireless networks. Finally, we present two particular applications of the theory for spectrum sharing of wireless channels with different qualities and user valuations, and discuss the economic properties of these auctions.

    Non-Uniform OFDM Pilot Power Distributions

    Speaker A Lee Swindlehurst (University of California, Irvine)
    Date 1:30 p.m. March 28th, 2013
    Location GWC 487
    Short Bio
    A. LEE SWINDLEHURST received the B.S. and M.S. degrees in Electrical Engineering from Brigham Young University, Provo, Utah, in 1985 and 1986, respectively, and the PhD degree in Electrical Engineering from Stanford University in 1991. From 1986-1990, he was employed at ESL, Inc., of Sunnyvale, CA, where he was involved in the design of algorithms and architectures for several radar and sonar signal processing systems. He was on the faculty of the Department of Electrical and Computer Engineering at Brigham Young University from 1990-2007, where he served as Department Chair from 2003-2006. From 2006-07, he was on leave working as Vice President of Research for ArrayComm LLC in San Jose, California. He is currently a Professor of Electrical Engineering and Computer Science at the University of California at Irvine. His research interests include sensor array signal processing for radar and wireless communications, detection and estimation theory, and system identification, and he has over 225 publications in these areas. Dr. Swindlehurst is a Fellow of the IEEE, past Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing, and past member of the Editorial Boards for several journals. He is a recipient of several paper awards: the 2000 IEEE W. R. G. Baker Prize Paper Award, the 2006 and 2010 IEEE Signal Processing Society’s Best Paper Award, and the 2006 IEEE Communications Society Stephen O. Rice Prize in the Field of Communication Theory.
    Abstract
    Prior research has established that for common FIR channel models, allocation of equally-powered and equally-spaced pilots is optimal for OFDM channel estimation. In this talk, we examine the following two situations where this result does not hold: (1) channel models parameterized by an unknown time-of-arrival in addition to FIR coefficients, and (2) applications where prior information is available about the channel, for example, in the form of a state-space or Gauss-Markov model. The first problem is useful in applications where one is interested in using the OFDM signal for both positioning and communications, and results a convex optimization problem that surprisingly has a sparse solution without being so constrained. The second problem can also be solved using convex optimization if a certain constraint is relaxed, and interestingly it can be shown that the optimal solution is obtained even without explicitly enforcing the constraint. Other problems that fall into this latter category will also be presented.

    Hummingbird: Privacy at the time of Twitter

    Speaker Gene Tsudik (University of California, Irvine)
    Date 2:00 p.m. April 5th, 2013
    Location USE 104 Direction
    Short Bio
    Gene Tsudik is a professor of Computer Science at the University of California, Irvine (UCI). He obtained his PhD in Computer Science from USC in 1991. Before coming to UCI in 2000, he was at IBM Zurich Research Laboratory (1991-1996) and USC/ISI (1996-2000). Over the years, his research interests included numerous topics in security, privacy and applied cryptography. Since 2009, he serves as the Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC).
    Abstract

    In the last several years, micro-blogging Online Social Networks (OSNs), such as Twitter, have taken the world by storm, now boasting over 100 million subscribers. As an unparalleled stage for an enormous audience, they offer fast and reliable centralized diffusion of pithy tweets to great multitudes of information-hungry and always-connected followers.

    At the same time, this information gathering and dissemination paradigm prompts some important privacy concerns about relationships between tweeters, followers and interests of the latter. In this talk, we assess privacy in today’s Twitter-like OSNs and describe an architecture and a trial implementation of a privacy-preserving service called Hummingbird. It is essentially a variant of Twitter that protects tweet contents, hashtags and follower interests from the (potentially) prying eyes of the centralized server. We argue that, although inherently limited by Twitter’s mission of scalable information sharing, this degree of privacy is valuable and viable. We demonstrate, via a working prototype, that Hummingbird’s additional costs are tolerably low. We also sketch out some enhancements that might offer better privacy in the long term.

    NOTE: joint work with Emiliano De Cristofaro (PARC) and Claudio Soriente (ETH Zurich)

    Visual Information Acquisition, Noisy Search, and Active Hypothesis Testing

    Speaker Tara Javidi (University of California, San Diego)
    Date 1:30 p.m. April 18th, 2013
    Location GWC 487
    Short Bio
    Tara Javidi studied electrical engineering at Sharif University of Technology, Tehran, Iran from 1992 to 1996. She received the MS degrees in electrical engineering (systems), and in applied mathematics (stochastics) from the University of Michigan, Ann Arbor, in 1998 and 1999, respectively. She received her Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, in 2002.
    Abstract

    Information acquisition problems form a class of stochastic decision problems in which a decision maker, by carefully controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about (Markovian) parameters of interest. Examples arise in patient care, computer vision, spectrum utilization, and joint source--channel coding. A generalization of hidden Markov models and a special case of partially observable Markov models, these are purely informational problems. In addition, due to the sequential nature of the problem, the decision maker relies on his current information state to constantly (re-)evaluate the trade-off between the precision and cost as well as the influence that every action has over the entire decision making horizon. In this talk, as a special case of information acquisition, we consider the problem of two-dimensional visual search as an active hypothesis testing problem. We first provide a brief survey of the corresponding literature on active hypothesis testing and design of experiments. In particular, we review the dynamic programming interpretation of information utility introduced by De Groot as well as the notion of asymptotic optimality due to Chernoff. We connect the stochastic control theoretic notion of information utility to the test reliability in statistics and information theory. We then underline the main drawback of Chernoff’s asymptotic optimality notion: his neglecting the complimentary role of the number of hypotheses, i.e. the resolution of visual search in our case. More precisely, we show that Chernoff's notion of asymptotic optimality falls short in showing the tension between using (asymptotically large number of) visual samples for a low resolution identification of the target with (asymptotically) high accuracy or a (asymptotically) high resolution of target identification with a lower degree of accuracy.

    To address the above shortcomings, we connect De Groot's information utility framework with the Shannon theoretic concept of uncertainty reduction and strengthen Chernoff's lower bound to account for the resolution of the search. This lower bound, as a corollary, provides upper bounds on maximum information acquisition rate and the optimal reliability as a function of rate. We also introduce Extrinsic Jensen–Shannon (EJS) divergence as a measure of information based on which a heuristic acquisition strategy is constructed. Via numerical and asymptotic analysis, the performance of the proposed policy, hence the utility of the EJS divergence in the context of two-dimensional visual search is investigated. In particular, under a mild technical condition, it is shown that the proposed heuristic achieves a strictly positive information acquisition rate with a strictly positive error exponent Furthermore, in the special case of uniform and mean-symmetric noise model, EJS policy is shown to achieve the upper bound on reliability function.

    This is joint work with Mohammad Naghshvar, Ofer Shayevitz, and Angela Yu.

    Secure Wireless File Distribution

    Speaker Anthony Ephremides (University of Maryland)
    Date 1:30 p.m. April 25th, 2013
    Location GWC 487
    Short Bio
    Anthony Ephremides holds the Cynthia Kim Eminent Professorship Chair of Information Technology. He holds a joint appointment with the Institute for Systems Research, of which he has been a founding member, and he is a also a member of and former Co-Director of the Maryland Hybrid Networks Center (HyNET), formerly known as the Center for Hybrid and Satellite Communication Networks (CHSCN). He received his B.S. degree in Electrical and Computer Engineering from the National Technical University of Athens, Greece, in 1967 and the M.A. and Ph.D. degrees also in Electrical Engineering from Princeton University in 1969 and 1971, respectively. He has served in many capacities in the IEEE and other organizations, from local organization posts to President of the Information Theory Society and member of the Institute Board of Directors, including Technical Program Chair and General Chair of Major Conferences.
    Abstract
    We consider a set of wireless nodes that possess subsets of a file and wish to exchange their content so that all nodes will possess the entire file. We want to do this with the fewest transmissions and we want to do it so as to prevent an eavesdropper from obtaining more than a threshold of knowledge about the file. We find that Network Coding presents a unique advantage in performing this task and we also assess the "cost" of security in terms of energy spent. To simplify the analysis we assume a shared broadcast channel without interference. This problems relates to old questions of distributed communication/computation initially investigated by Andrew Yao. Possible new questions that arise are being considered, as well.

    Block Regularized Lasso for Multivariate Multi-Response Linear Regression

    Speaker Yingbin Liang (Syracuse University)
    Date 1:30 p.m. May 2nd, 2013
    Location GWC 487
    Short Bio

    Dr. Yingbin Liang received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005. In 2005-2007, she was working as a postdoctoral research associate at Princeton University. In 2008-2009, she was an assistant professor at the Department of Electrical Engineering at the University of Hawaii. Since December 2009, she has been an assistant professor at the Department of Electrical Engineering and Computer Science at the Syracuse University. Dr. Liang's research interests include machine learning, information theory, wireless communications and networks.

    Dr. Liang was a Vodafone Fellow at the University of Illinois at Urbana-Champaign during 2003-2005, and received the Vodafone-U.S. Foundation Fellows Initiative Research Merit Award in 2005. She also received the M. E. Van Valkenburg Graduate Research Award from the ECE department, University of Illinois at Urbana-Champaign, in 2005. In 2009, she received the National Science Foundation CAREER Award, and the State of Hawaii Governor Innovation Award.
    Abstract

    Linear regression, as a basic statistical problem, has found its applications in almost all science and engineering fields. A challenging regime of this problem is when the number of samples is much smaller than the number of regression vectors. Excitingly, a powerful technique Lasso has recently been introduced to solve the problem in high dimensional regime with theoretic guarantee if the regression vector is sparse enough.

    In this talk, I will first give an overview of the existing results on applying Lasso for solving high dimensional linear regression problems, following which I will introduce the multivariate multi-response (MVMR) linear regression model that we study. One major advantage of our model lies in jointly studying multiple regression problems (i.e., tasks) together, which can reduce sample complexity significantly. I will then present our main results. Namely, we characterize sufficient and necessary conditions under which l_1\l_2 regularized Lasso successfully recovers the support union of the MVMR model. These conditions admit a sharp threshold transition on the sample size. In particular, we analytically characterize the impact of sparsity of regression vectors and the number of tasks on the sample complexity, which quantitatively demonstrates the advantage of multi-task learning. I will finally talk about the implications of our results, its comparison to existing results, and our numerical results.

    How To Clean a Slate

    Speaker John Day (Boston University)
    Date 1:00 p.m. May 13th, 2013
    Location GWC 487
    Short Bio
    John Day has been involved in research and development of computer networks since 1970, when their group at the University of Illinois was the 12th node on ARPANet (precursor to the Internet) and has developed and designed protocols for everything from the data link layer to the application layer. Also making fundamental contributions to research on distributed databases. He managed the development of the OSI reference model, naming and addressing, and a major contributor to the upper-layer architecture.  He was a major contributor to the development of network management architecture, working in the area since 1984 and building and deploying a network management system, a decade ahead of comparable systems. Mr. Day has published Patterns in Network Architecture: A Return to Fundamentals (Prentice Hall, 2008), which has been characterized (embarrassingly) as “the most important book on network protocols in general and the Internet in particular ever written.”  The book analyzes the fundamental flaws in the Internet and proposes what appears to be the only path forward. Today Mr. Day splits his time between making this new path a reality and teaching at Boston University. Mr. Day is also a recognized scholar in the history of cartography, and contributed to exhibits at the Smithsonian, and is currently President of the Boston Map Society.
    Abstract
    Quite remarkably, there is a simple solution to the problems of network architecture described in the previous lecture. While the fundamentals have been known for decades, there are some new patterns that we should have seen but didn’t. In this lecture, we will see how the simple idea that Networking is Interprocess Communication combined with a three or four other fundamental results. Yields a simple, but powerful architecture consisting of a single repeating layer and only two protocols: one for data transfer and one for applications, where multihoming and mobility are inherent in the structure, i.e. free; where router table size can be bounded and large addresses are not required; is fundamentally more secure and with less cost, etc. etc. We are still uncovering properties of the model we didn’t know were there for things that today require multiple protocols and complex solutions but just fall out naturally.

    Coding and Information-Theoretic Aspects of Coordination in Networks

    Speaker Joerg Kliewer (New Mexico State University)
    Date 1:00 p.m. May 21st, 2013
    Location GWC 487
    Short Bio

    Dr. Joerg Kliewer received the Dipl.-Ing. (MSEE) degree in Electrical Engineering from the Hamburg University of Technology, Hamburg, Germany, in 1993 and the Dr.-Ing. degree (Ph.D.) in Electrical Engineering from the University of Kiel, Kiel, Germany, in 1999, respectively.

    From 1993 to 1998 he was a Research Assistant at the University of Kiel, Germany, and from 1999 to 2004, he was a Senior Researcher and Lecturer with the same institution. In 2004, he visited the University of Southampton, Southampton, U.K., for one year, and from 2005 until 2007, he was with the University of Notre Dame, Notre Dame, IN, as a Visiting Assistant Professor. In August 2007, he joined New Mexico State University, Las Cruces, NM, as an Assistant Professor. His research interests include error-correcting codes, wireless communications, communication networks, and information theory.

    Dr. Kliewer was the recipient of a Leverhulme Trust Award and a German Research Foundation Fellowship Award in 2003 and 2004, respectively. He is a Member of the Editorial Board of the EURASIP Journal on Advances in Signal Processing and is an Associate Editor of the IEEE Transactions on Communications.
    Abstract

    We consider the coordination of multiagent systems over point-to-point channels and small multiterminal networks. Rather than distributing explicit messages to coordinate the behavior of different agents, we specify coordination by means of an achievable joint distribution between the actions of the agents and communicate only that amount of information needed to achieve a given joint target distribution. In particular, in this talk we address strong coordination where the target joint distribution and the joint distribution induced by a coordination code are statistically indistinguishable.

    We first study the problem of coordination in a three-terminal line network, in which agents use common randomness and provide inner and outer bounds to the coordination capacity region. Specifically, we show that common randomness helps to achieve optimal communication rates between agents, and that matching the network topology to the behavior structure may reduce inter-agent communication rates. We then consider a practical coordination code based on polar codes for a two-node network in which the action imposed by nature at the source node is binary and uniform. By exploiting the connection between channel resolvability and strong coordination and the observation that polar codes are useful for channel resolvability over binary symmetric channels, we prove that nested polar codes achieve a subset of the strong coordination capacity region. Therefore these codes provide a constructive and low complexity solution for strong coordination.

    Maximum pressure policies for stochastic processing networks

    Speaker Jim Dai (Cornell University)
    Date 1:30 p.m. January 9th, 2014
    Location GWC 487
    Short Bio

    Jim Dai is a professor in the School of Operations Research and Information Engineering (ORIE) of Cornell University. He is currently on leave from the Chandler Family Chair of Industrial & Systems Engineering at Georgia Institute of Technology, where he has been a faculty member since 1990. For more than twenty years, he has worked on stochastic models arising from communications, manufacturing, and service systems that include data switches, semiconductor wafer fabrication lines, call centers, and healthcare-delivery systems.

    Jim Dai received B.A. and M.S. degrees from Nanjing University and a Ph.D. degree from Stanford University. He is an elected fellow of Institute of Mathematical Statistics and an elected fellow of Institute for Operations Research and the Management Sciences (INFORMS). His awards for research contributions include the Best Publication Award in 1997 and The Erlang Prize in 1998, both from the Applied Probability Society of INFORMS. He delivered the Markov Lecture at INFORMS national meeting in October 2012. He is the Editor-in-Chief for Mathematics of Operations Research, a past Area Editor for Operations Research, and a past Series Editor for Handbooks in Operations Research and Management Science.
    Abstract

    Stochastic processing networks have been introduced in a series of three papers by Harrison (2000, 2002, 2003). A processing network is a system that takes materials of various kinds as inputs, and uses processing resources to produce other materials as outputs. These networks model complex systems including semiconductor wafer fabrication facilities, networks of data switches, and large-scale call centers. Key performance measures of such a network include throughput and average cycle time. Elements of an operational policy may include input control, sequencing, and routing; the choice of such a policy can dramatically affect network performance.

    In this talk, we will first show that even in simple networks, commonly used operational policies such as first-in-first-out sequencing may perform badly, failing to achieve even "throughput optimality." We then introduce a family of policies known as maximum pressure policies. Such a policy needs only local or semi-local congestion information to be implemented. Often, its implementation does not require arrival rate information which can be difficult to be estimated reliably.

    We show that maximum pressure policies are always throughput optimal, regardless of the processing network's topology or parameter values. Such a policy is further shown to asymptotically minimize a certain diffusion-scaled quadratic holding cost when the network satisfies a heavy traffic condition and a complete resource pooling condition.

    This talk is based on joint works with Wuqin Lin at Kellogg School of Business of Northwestern University.

    Scheduling Real-Time Traffic in Wireless Ad Hoc Networks

    Speaker Lei Ying (Arizona State University)
    Date 1:30 p.m. January 30th, 2014
    Location GWC 487
    Short Bio

    Lei Ying received his B.E. degree from Tsinghua University, Beijing, his M.S. and Ph.D in Electrical Engineering from the University of Illinois at Urbana-Champaign. He currently is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University, and an Associate Editor of the IEEE/ACM Transactions on Networking.

    His research interest is broadly in the area of stochastic networks, including big data and cloud computing, cyber security, P2P networks, social networks and wireless networks.
    Abstract
    This presentation considers the problem of scheduling real-time traffic in wireless ad hoc networks. We consider an ad hoc wireless network with general interference and general one-hop traffic. Each packet is associated with a deadline and will be dropped if not being transmitted before the deadline expires. The number of arrivals in each time slot and the length of a deadline are both stochastic and follow certain distributions. We only allow a fraction of packets to be dropped. At each link, we assume the link keeps track of the difference between the minimum number of packets that need to be delivered and the number of packets that are actually delivered, which we call deficit. The largest-deficit-first (LDF) policy schedules links in descending order according to their deficit values, which is a variation of the largest-queue-first (LQF) policy for non-real-time traffic. I will show that the efficiency ratio of LDF can be lower bounded by a quantity that we call the real-time local-pooling factor (R-LPF). I will further present that given a network with interference degree β, the R-LPF is at least 1/(β+1), which in the case of the one-hop interference model translates into an R-LPF of at least 1/3.

    Evaluating Mobile Video and Mobile Applications

    Speaker Prasant Mohapatra (University of California at Davis)
    Date 11:00 a.m. February 14th, 2014
    Location GWC 487
    Short Bio

    Dr. Prasant Mohapatra is currently a Professor in the Department of Computer Science at the University of California, Davis. He served as the Chair of the Computer Science Department from 2007-2013, and is current the Interim Vice-Provost of UC Davis. He is the Editor-in-Chief of the IEEE Transactions on Mobile Computing. He was on the editorial board of the IEEE Transactions on Computers, IEEE Transactions on Mobile Computing, IEEE Transaction on Parallel and Distributed Systems, ACM WINET, and Ad Hoc Networks. He has chaired and has been on the program/organizational committees of several international conferences.

    Dr. Mohapatra is a Fellow of the IEEE and a Fellow of AAAS. He received his doctoral degree from Penn State University in 1993, and received an Outstanding Engineering Alumni Award in 2008. He also received an Outstanding Engineering Faculty award from UC Davis, and the HP Lab Innovation Award.

    Dr. Mohapatra's research interests are in the areas of wireless and mobile networks, security, and quality of experience in both wired and wireless networks. His research has been funded through grants from the National Science Foundation, Department of Defense, Intel Corporation, Siemens, Panasonic Technologies, Hewlett Packard, Raytheon, AT&T, Huawei Technologies, and EMC Corporation.
    Abstract
    This talk will be focused on two aspects of the expanding usage of mobile applications, services, and devices. The first part of the talk will deal with the mobile video quality assessment, while the second part will provide insights to the smartphone applications and their overheads. Assessment of mobile videos raises unique challenges due to the unavailability of original videos, the limited computation power of mobile devices and the inherent characteristics of wireless networks (packet loss and delay). We will present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. We use the TVM values to derive a reduced-reference temporal quality assessment metric, Temporal Variation Index (TVI), which quantifies the quality degradation incurred in network trans-mission. We show that TVI can also estimate the network conditions such as packet loss and delay. In the second part of the talk, we focus on identifying the overhead traffic that is generated by the free apps with respect to the paid apps. The goal of this work is to quantify the cost of the overhead traffic of the popular free apps and compare it with the paid apps. We have developed an intricate methodology for identifying and measuring the bandwidth requirements of the overheads associated with the free apps. Through comprehensive measurements, we have shown that in most cases, the paid versions of the apps will indeed be a fraction of the cost to the end users when compared to the actual cost of the free versions.

    Coherence in its many guises

    Speaker Louis Scharf (Colorado State University)
    Date 1:30 p.m., February 27th, 2014
    Location GWC 487
    Short Bio

    Louis Scharf received his Ph.D. from the University of Washington, Seattle. From 1971 to 1982 he served as Professor of Electrical Engineering and Statistics at CSU. From 1982 to 1985 he was Professor and Chairman of Electrical and Computer Engineering at the University of Rhode Island, Kingston. From 1985 to 2000 he was Professor of Electrical and Computer Engineering at the University of Colorado, Boulder. In January 2001, Professor Scharf rejoins Colorado State University as Professor of Electrical and Computer Engineering, and Statistics.

    Professor Scharf has held several visiting positions here and abroad. He has developed particularly close ties with Ecole Superieure d'Electricite (Gif-sur-Yvette), Ecole Nationale Superieure des Telecommunications (Paris), and EURECOM (Nice). He is a recognized expert in statistical signal processing, as it applies to adaptive radar, sonar, and wireless communication. His most important contributions to date are to invariance theories for detection and estimation; matched and adaptive subspace detectors for radar, sonar, and data communication; and canonical decompositions for reduced dimensional filtering and quantizing. His current interests are in rapidly-adaptive receiver design for space-time signal processing in the wireless communication channel.

    Professor Scharf is a Fellow of IEEE. He chairs the Fellow Committee for the IEEE Signal Processing Society, and serves on its Technical Committees for Theory and Methods and for Sensor Arrays and Multichannel Signal Processing. He has received numerous awards for his research contributions to statistical signal processing, including an IEEE Distinguished Lectureship, an IEEE Third Millenium Medal, and the Technical Achievement Award from the IEEE Signal Processing Society.
    Abstract
    This talk surveys various historical notions of coherence, including Hadamard, Hilbert, and Euclidean concepts of coherence, and describes how these have manifested in statistical signal processing over the past few decades. It proceeds to examine the proposition that coherence is an organizing principle that underpins much of statistical signal theory, and that the geometric nature of coherence permeates the subject.

    Exact Asymptotics and Moderate Deviations in Channel Coding

    Speaker Aaron B. Wagner (Cornell University)
    Date 11:00 a.m., March 27th, 2014
    Location GWC 487
    Short Bio
    Aaron Wagner is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University. He received the B.S. degree from the University of Michigan, Ann Arbor, and the M.S. and Ph.D. degrees from the University of California, Berkeley. During the 2005-2006 academic year, he was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor in the School of Electrical and Computer Engineering at Cornell. He has received the NSF CAREER award, the David J. Sakrison Memorial Prize from the U.C. Berkeley EECS Dept., the Bernard Friedman Memorial Prize in Applied Mathematics from the U.C. Berkeley Dept. of Mathematics, and teaching awards at the Department, College, and University level.
    Abstract

    Information-theoretic results describing the limits of reliable communication over noisy channels are typically asymptotic in the block length. In practice, however, small block lengths are desirable and thus the speed of convergence of these characterizations is important. Classical results show that the error probability converges to zero exponentially fast with the block length if the data rate is fixed. But this exponent is very small in the regime of practical interest, so the subexponential "pre-factor" plays an important role. Yet very little is known about the pre-factor.

    Using techniques from probability theory, convex optimization, and information theory, we characterize the order of the pre-factor for all but a degenerate class of channels; for this class, the results tightly bound the order. I will also discuss how our techniques can be used to obtain improved results for the "normal approximation" regime. Finally, I will advocate eschewing both error exponents and the normal approximation in favor of the "moderate deviations" regime in which the rate approaches capacity and the error probability tends to zero simultaneously.

    This is joint work with Yucel Altug.

    Wind Aggregation via Risky Power Markets

    Speaker Yue Zhao (Princeton University and Stanford University)
    Date 1:30 p.m., April 10th, 2014
    Location GWC 487
    Short Bio
    Yue Zhao (S’06–M’11) received the B.E. degree in electronic engineering from Tsinghua University, Beijing, China, in 2006, and the M.S. and Ph.D. degrees in electrical engineering, both from the University of California, Los Angeles (UCLA), Los Angeles, in 2007 and 2011, respectively. He is currently a Postdoctoral Scholar with the Departments of Electrical Engineering at Stanford University and Princeton University. His research interests include sustainable energy system, smart grid, infrastructure resilience and security, optimization theory, signal processing, and information theory.
    Abstract
    Uncertainty and variability of renewable energy generation imposes great challenges on reliable operation of the electricity grid with high renewable penetration. Aggregation of diverse renewable energy sources can effectively reduce their uncertainty and variability. We develop a new market mechanism for renewable energy producers to achieve flexible and distributed aggregation. In this market, we introduce a novel risky (in addition to riskless) power forward contract that allows renewable energy producers to trade uncertain power output, so that their own risks are reduced. We show that the risky power market has a unique competitive equilibrium, characterized in closed form. Moreover, the market equilibrium enjoys a number of efficiency, fairness and stability properties that make the risky power market very appealing.

    Communication, Control and Sensing: Connecting Communication over Channels with State with the Witsenhausen Counterexample for Distributed Control

    Speaker Urbashi Mitra (University of Southern California)
    Date 10:45 a.m., April 16th, 2014
    Location GWC 487
    Short Bio
    Urbashi Mitra received the B.S. and the M.S. degrees from the University of California at Berkeley and her Ph.D. from Princeton University. After a six year stint at the Ohio State University, she joined the Department of Electrical Engineering at the University of Southern California, Los Angeles, where she is currently a Professor. She is a member of the IEEE Information Theory Society's Board of Governors (2002-2007, 2012-2014) and the IEEE Signal Processing Society's Technical Committee on Signal Processing for Communications and Networks (2012-2014). She is the recipient of: 2012 Globecom Signal Processing for Communications Symposium Best Paper Award, 2012 NAE Lillian Gilbreth Lectureship, USC Center for Excellence in Research Fellowship (2010-2013), the 2009 DCOSS Applications & Systems Best Paper Award, IEEE Fellow (2007), Texas Instruments Visiting Professor (Fall 2002, Rice University), 2001 Okawa Foundation Award, 2000 OSU College of Engineering Lumley Award for Research, 1997 OSU College of Engineering MacQuigg Award for Teaching, and a 1996 National Science Foundation (NSF) CAREER Award. Dr. Mitra has been/is an Associate Editor for the following IEEE publications: Transactions on Signal Processing (2012--), Transactions on Information Theory (2007-2011), Journal of Oceanic Engineering (2006-2011), and Transactions on Communications (1996-2001). Dr. Mitra has held visiting appointments at: the Delft University of Technology, Stanford University, Rice University, and the Eurecom Institute. She served as co-Director of the Communication Sciences Institute at the University of Southern California from 2004-2007. Her research interests are in: wireless communications, communication and sensor networks, detection and estimation and the interface of communication, sensing and control.
    Abstract
    Traditionally, channel estimation has been undertaken only in service of better data communication. However, a number of problem frameworks (sonar, cognitive radio, digital watermarking) require the reconstruction of a transmitted message as well as estimating properties of the channel over which the message was transmitted. We abstract these scenarios to one wherein a source sends a message to the destination and the destination endeavors to both decode the message and estimate the channel to some fidelity. The performance of such a system can be captured by the capacity-distortion function that characterizes the fundamental tradeoff between transmission rate and state estimation distortion. We compute the capacity-distortion function for the three cases of channel knowledge at the transmitter. We connect these results to the information theoretic version of the celebrated Witsenhausen counter-example for distributed control. Witsenhausen underscores the complexity of distributed systems by showing that even for a two-stage distributed LQG system, a non-linear control strategy can outperform all linear laws. We can exploit our results for communication over channels with state to compute the minimum distortion for the information theoretic version of the Witsenhausen counter-example.

    Online Learning in Communication Networks

    Speaker Bhaskar Krishnamachari (University of Southern California)
    Date 1:30 p.m., May 8th, 2014
    Location GWC 487
    Short Bio

    Bhaskar Krishnamachari is Associate Professor and Ming Hsieh Faculty Fellow in Electrical Engineering at the Viterbi School of Engineering at the University of Southern California. He also holds a joint appointment in Computer Science.

    His research interests are focused on the design and analysis of algorithms, protocols, and applications for next generation wireless networks including vehicular networks, sensor networks, cognitive radio networks, and green cellular networks. On these topics, his research spans the entire spectrum from theoretical analysis of algorithms to prototype software implementations of network protocols and applications. He has co-authored over 200 technical articles on these topics, including four that have received conference best-paper awards at ACM/IEEE IPSN (2004, 2010), ACM MSWiM (2006) and ACM MobiCom (2010). He has also authored a text titled Networking Wireless Sensors, published by Cambridge University Press, and was a Co-editor of the 2012 landmark theme issue on Sensor Network Algorithms and Applications published in the Philosophical Transactions of the Royal Society A.

    In 2011, Bhaskar Krishnamachari was included in the TR-35, Technology Review Magazine's annual listing of the top 35 young innovators under the age of 35. He has also received the ASEE Terman Award, and the National Science Foundation CAREER award. He obtained his B.E. in Electrical Engineering at The Cooper Union for the Advancement of Science and Art in New York City in 1998, and then attended Cornell University where he received his M.S. in Electrical Engineering (1999), and his Ph.D. in Electrical Engineering (2002).
    Abstract

    Online learning and sequential decision-making in the face of uncertainty is encountered in many aspects of communication networks. I present some recent work on this topic. Specifically, I will discuss work we have done on combinatorial multi-armed bandits, and on the multi-period newsvendor problem.

    Multi-armed bandit problems arise whenever there are multiple choices from which one must be selected at each time, each yielding a stochastic reward. If the underlying reward distribution is unknown, the goal is to design a policy that balances exploration and exploitation to learn efficiently from observations. The metric of interest is regret, defined as the cumulative gap in reward with respect to an omniscient genie. We show that for combinatorial problems with linear reward functions and individual observations commonly encountered in networking, it is possible to have a policy whose regret is not only sublinear in time (resulting in asymptotically optimal time-averaged reward) but also polynomial in the network size.

    In the second part of the talk I describe ongoing work on the multi-period news vendor problem, which can be applied to rate adaptation over Markovian channels. The goal is to track a Markovian demand process with inventory decisions (supply) so as to maximize the total expected reward. The demand process is observed exactly whenever the supply exceeds the demand at the expense of an overstocking penalty, while insufficient supply results in partial observations and loss of profit. While this problem is generally intractable, we prove some structural results on the optimal solution, and suggest efficient heuristics.

    The work described in this talk has been done in collaboration with Parisa Mansourifard, Yi Gai, Tara Javidi, and Raul Jain.

    Social Group Utility Maximization for Mobile Social Networks

    Speaker Junshan Zhang (Arizona State University)
    Date 1:30 p.m. September 5th, 2014
    Location GWC 487
    Short Bio
    Junshan Zhang joined the School of ECEE at Arizona State University in August 2000. His interests include cyber-physical systems, communications networks, and network science. His current research focuses on fundamental problems in information networks and energy networks, including stochastic modeling and optimization for smart grid, network optimization/control, mobile social networks, crowdsourcing, cognitive radio, and network information theory.
    Abstract
    Both mobile networks and social networks are projected to continue growing rapidly in the foreseeable future. A salient feature of mobile networks is that mobile devices are carried and operated by human beings. With this insight, we develop a social group utility maximization (SGUM) framework that takes into account both social coupling and physical coupling among mobile users. Under the SGUM framework, each user aims to maximize its social group utility, defined as the weighted sum of the individual utilities of the users that have social ties with it. One distinctive merit of the SGUM framework is that it provides a unifying platform to capture complex social structure among mobile users, consisting of diverse “positive” and “negative” social ties, and hence it offers rich flexibility in modeling and understanding the rich continuum from zero-sum game (ZSG) to non-cooperative game (NCG) to network utility maximization (NUM) - traditionally disjoint paradigms for network management. We will also touch upon related privacy and security issues.

    Estimation and Registration on Graphs

    Speaker Douglas Cochran (Arizona State University)
    Date 1:30 p.m. September 12th, 2014
    Location GWC 487
    Short Bio
    Douglas Cochran joined the the ASU faculty in 1989. Before coming to ASU, he was a senior scientist at BBN Laboratories. Professor Cochran has served as program manager for mathematics in the U.S. Defense Advanced Research Projects Agency, as a consultant for the Australian Defense Science and Technology Organisation, as associate editor of the IEEE Transactions on Signal Processing, and as general co-chair of the 1999 IEEE International Conference for Acoustics, Speech and Signal Processing and the 1997 U.S.-Australia Workshop on Defense Signal Processing.
    Abstract
    This talk will introduce a statistical framework for a broad class of problems involving synchronization or registration of data across a sensor network in the presence of noise. This framework enables an estimation-theoretic approach to the design and characterization of synchronization algorithms. The Fisher information is expressed in terms of the distribution of the measurement noise and standard mathematical descriptors of the network’s graph structure for several important cases. This leads to maximum likelihood and approximate maximum-likelihood registration algorithms and also to distributed iterative algorithms that, when they converge, attain ML solutions. The relationship between estimation in this setting and Kirchhoff’s laws will also be elucidated. This is joint work with Steve Howard and Bill Moran.

    Dynamic Mathematical Modeling of Information Diffusion in Online Social Networks

    Speaker Feng Wang (Arizona State University)
    Date 1:30 p.m. September 19th, 2014
    Location GWC 487
    Short Bio
    Feng Wang is currently an associate professor in the School of Mathematical and Natural Sciences at Arizona State University. She received her BS degree from Wuhan University in 1996, MS degree from Peking University in 1999, and PhD degree from the University of Minnesota, Twin Cities in 2005, all in computer science. Her research interests revolve around network modeling, optimization, and analysis of various networks, including online social networks, wireless networks, and Internet backbone and data center networks. She is a member of the IEEE.
    Abstract

    Recent years have witnessed the explosive growth of online social networks that connect people in the digital world. One of the most important applications of online social networks is to create and spread information such as latest news headlines, celebrity tweets, product marketing, movie recommendations, political campaign advertisement, or spam messages across the networks. In light of the significant role online social networks have played in recent events and crisis, it has become increasingly urgent to understand the diffusion process of information spreading over social interactions. However, modeling and analysis of information diffusion in online social networks are challenging due to the intricacy of human dynamics and social interactions, the obscure underlying diffusion network structures, the vast scale of online social networks, and the heterogeneity and diversity of social media.

    In this talk, I will present an innovative approach, dynamic mathematical modeling, to explore information diffusion from both spatial and temporal dimensions in order to reveal details on how information propagates through individuals at different distances from the information source. Dynamic mathematical models have been extensively applied in disease spreading in epidemics, opinion formation in economics, and information spreading in traditional sociological systems due to its unique capabilities of accommodating dynamics and analyzing the global characteristics of a network without prior knowledge of underlying structures and exact interaction models between individuals. I will present a linear diffusive model based on partial differential equations to characterize the spatio-temporal information diffusion in online social networks and demonstrate the accuracy of the model verified with real datasets collected from a news aggregation website, Digg.

    A Boundedly Rational User Equilibrium Model for the Traffic Assignment Problem

    Speaker Yingyan Lou (Arizona State University)
    Date 1:30 p.m. September 26th, 2014
    Location ISTB1-227
    Short Bio
    Dr. Yingyan Lou is an assistant professor in the Civil, Environmental, and Sustainable Engineering program in the School of Sustainable Engineering and The Built Environment Engineering at Arizona State University. She holds a B.S. and a B.A.Econ degree from Beijing University, and received her M.S. and Ph.D. degrees in Civil and Coastal Engineering from the University of Florida. Before ASU, she worked at the Department of Civil, Construction and Environmental Engineering at the University of Alabama. Dr. Lou’s primary area of expertise is transportation systems modeling and optimization, with applications in various transportation planning, management and operations problems, such as connected vehicle applications in traffic operations, system-wide congestion pricing, traffic-responsive tolling for managed-lane operations, dynamic origin-destination demand estimation, robust transportation network design, freeway incident response planning, and infrastructure asset management. She also has a keen interest in traffic flow theory. Her research has led to 18 journal publications, 1 book chapter, and 14 peer-reviewed conference articles. In 2010, she received the Pikarsky Award for Outstanding Ph.D. Dissertations in Science and Technology from the Council of University Transportation Centers. Dr. Lou currently serves as a member in three Transportation Research Board committees (Transportation Network Modeling, User Information Systems, and Highway Safety Performance).
    Abstract

    This research investigates flow distributions in static transportation networks with travelers’ boundedly rational route-choice behaviors. Under such behavior, users do not necessarily choose a shortest or cheapest route, when doing so does not reduce their travel time by a significant amount. A general path-based definition and a more restrictive link-based representation of boundedly rational user equilibrium (BRUE) are presented. The set of BRUE flow distributions are non-convex and always non-empty. The existence of alternative BRUE flow distributions implies uncertainties in the outcome of certain transportation policies and plans. Numerical experiments demonstrate that system performance may vary substantially within the BRUE set. The problems of finding best- and worst-case BRUE flow distributions are formulated and solved as mathematical programs with complementarity constraints. This research also explores robust approaches for transportation planning to guard against the worst-case scenario.

    This research offers a new alternative for more realistically modeling of users’ route choices in general networks. It relaxes the dominance of the perfect rationality assumption in transportation planning, and leads to models and tools that are more realistic and accurate to help and guide in the analysis and evaluation of transportation planning decisions.

    Design and stability of load-side frequency control

    Speaker Steven H. Low (California Institute of Technology)
    Date 1:30 p.m. October 3rd, 2014
    Location GWC 487
    Short Bio
    Steven H. Low is a Professor of the Department of Computing & Mathematical Sciences and the Department of Electrical Engineering at Caltech. He was a co-recipient of IEEE best paper awards, the R&D 100 Award, and an Okawa Foundation Research Grant. He is on the Technical Advisory Board of Southern California Edison and an IEEE Fellow. He received his B.S. from Cornell and PhD from Berkeley, both in EE.
    Abstract

    We present a systematic method to design ubiquitous continuous fast-acting distributed load control for frequency regulation in power networks, by formulating an optimal load control (OLC) problem where the objective is to minimize the aggregate cost of tracking an operating point subject to power balance over the network. We prove that the swing dynamics and the branch power flows, coupled with frequency-based load control, serve as a distributed primal-dual algorithm to solve OLC. We show that load-side primary frequency control that rebalance power and resynchronize frequency can be implemented in a completely decentralized manner because the local frequency deviations at each bus convey exactly the right information about the global power imbalance for the loads to make individual decisions that turn out to be globally optimal. Load-side secondary frequency control that restores the nominal frequency and scheduled inter-area flows can be implemented in a distributed manner using message passing among neighbors. Simulations show that the proposed algorithms also improve the transient performance.

    (Joint work with Changhong Zhao and Enrique Mallada (Caltech), Ufuk Topcu (UPenn), and Lina Li (MIT/Harvard))

    Agnostic Protocol Translation for Cross-Domain Information sharing

    Speaker Chen Liu (UtopiaCompression Corporation)
    Date 3:00 p.m. October 7th, 2014
    Location GWC 487
    Short Bio
    Dr. Chen Liu has ten years of R&D experience in Networking and Distributed High-Performance Computing. She received her PhD in Computing Science from University of Alberta, Canada. Currently, as a Research and Development Scientist at UtopiaCompression Corporation, she focuses on researching and developing new networking technologies for TCP/IP and wireless mobile networks. At UtopiaCompression, she has led and contributed to multiple projects including SDN-based cross-layer protocol design for next-generation satellite networks, agnostic protocol translation for cross-domain communication, and network optimization mechanisms for dynamic mission-driven traffic prioritization. Recently, her contributions were recognized by IEEE-Eta Kappa Nu honor society.
    Abstract
    Network translation gateways can provide seamless interoperability among different airborne, ground and maritime domains. Effective interconnection between waveforms and protocols through the gateways requires manual intensive development and specialized protocol expertise. Therefore, enabling building versatile gateways that can effectively translate those protocols across different network domains is of utmost importance to improve system interoperability. High-level domain specific languages have been utilized to support agnostic protocol interoperability. However, protocol-specific knowledge specification, the core of protocol translation, is still left to protocol experts with manual coding without advanced tools in support of simplification, guidance or verification. Such a manual and unsupervised method of generating translation logic is complex, time consuming and error-prone. In order to overcome these problems with much more productive gateway development, we propose a novel, visual protocol-agnostic translation toolkit. This toolkit offers three advanced features: 1) simple, intuitive visualized specification of protocol-specific knowledge; 2) graph-based translation logic verification; and 3) automatic gateway generation. The first two features have not been addressed in currently available protocol translation solutions.

    Joint Radar-Communications Performance Inner Bounds: Data versus Estimation Information Rates

    Speaker Daniel Bliss (Arizona State University)
    Date 1:30 p.m. October 17th, 2014
    Location GWC 487
    Short Bio

    Daniel W. Bliss is an Associate Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. Dan received his Ph.D. and M.S. in Physics from the University of California at San Diego (1997 and 1995), and his BSEE in Electrical Engineering from Arizona State University (1989). His current research topics include statistical signal processing, multiple-input multiple-output (MIMO) wireless communications, MIMO radar, cognitive radios, radio network performance bounds, geolocation techniques, channel phenomenology, and signal processing and machine learning for anticipatory physiological monitoring. Dan has been the principal investigator on numerous programs with applications to radio, radar, and medical monitoring. He has made significant contributions to robust multiple-antenna communications including important theoretical results, multiple patents, and the development of advanced fieldable prototype systems. He is responsible for some of the seminal MIMO radar literature, and was the principal investigator on an experimental airborne ground moving target indicator (GMTI) MIMO radar that demonstrated the validity of the theoretical results.

    Before moving to ASU Dan was a senior member of the technical staff at MIT Lincoln Laboratory (1997-2012) in the Advanced Sensor Techniques group. Between his undergraduate and graduate degrees Dan was employed by General Dynamics (1989-1991), where he designed avionics for the Atlas-Centaur launch vehicle, and performed research and development of fault-tolerant avionics. As a member of the superconducting magnet group at General Dynamics (1991-1993), he performed magnetic field calculations and optimization for high-energy particle-accelerator superconducting magnets. His doctoral work (1993-1997) was in the area of high-energy particle physics, searching for bound states of gluons, studying the two-photon production of hadronic final states, and investigating innovative techniques for lattice-gauge-theory calculations. He is a senior member of the IEEE. He has published over 70 technical articles and conference papers, and he received the Best Lecture Award for his 2008 Tri-Service radar paper that discussed MIMO radar.
    Abstract
    We investigate cooperative radar and communications signaling. Each system typically considers the other system a source of interference. Consequently, the traditional solution is to isolate the two systems spectrally or spatially. By considering the radar and communications operations to be a single joint system, we derive performance bounds on a receiver that observes communications and radar return in the same frequency allocation. Bounds on performance of the joint system are measured in terms of data information rate for communications and a novel radar estimation information rate parameterization for the radar. We construct inner bounds on the limiting performance.

    A New Geometric Approach to Topic Modeling and Discovery

    Speaker Prakash Ishwar(Boston University)
    Date 1:30 p.m. October 31st, 2014
    Location GWC 487
    Short Bio
    Prakash Ishwar received the BTech degree in EE from IIT Bombay in 1996 and the MS and PhD degrees in ECE from UIUC in 1998 and 2002 respectively. After two years as a post-doctoral researcher in the EECS department at UC Berkeley, he joined the faculty of Boston University where he is currently Associate Professor of Electrical and Computer Engineering. His research interests are in information theory, information-theoretic security, statistical signal processing, machine learning, and visual information analysis and processing.
    Abstract
    In this talk I will present a new algorithm for topic discovery based on the geometry of cross-document word-frequency patterns. The geometric perspective gains significance under the so called separability condition that posits the existence of novel-words that are unique to each topic. The algorithm utilizes random projections to identify novel words and associated topics. The key insight here is that the maximum and minimum values of cross-document frequency patterns projected along any direction are associated with novel words. In contrast to ML and Bayesian approaches that require solving non-convex optimization problems using approximations or heuristics, the new algorithm is convex, asymptotically consistent, and has provable performance guarantees. While our sample complexity bounds for topic recovery are similar to the state-of-art, the computational complexity of our scheme scales linearly with the number of documents and the number of words per document. We present several experiments on synthetic and realworld datasets to demonstrate qualitative and quantitative merits of our scheme. This talk is based on joint work with Ding, Rohban, and Saligrama at Boston University.

    When SDN Meets Security: New Opportunities and Challenges

    Speaker Guofei Gu (Texas A&M University)
    Date 2:30 p.m. November 6th, 2014
    Location GWC 487
    Short Bio
    Dr. Guofei Gu is an associate professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU). Before coming to Texas A&M, he received his Ph.D. degree in Computer Science from the College of Computing, Georgia Institute of Technology. His research interests are in network and system security, such as Internet malware analysis/detection/defense, software-defined networking security, web and social network security, mobile and Android security, and intrusion/anomaly detection. Dr. Gu is a recipient of 2010 NSF CAREER Award, 2013 AFOSR Young Investigator Award, 2010 IEEE Symposium on Security & Privacy (S&P'10) Best Student Paper Award, and a Google Faculty Research Award. He is currently directing the SUCCESS (Secure Communication and Computer Systems) Lab at TAMU.
    Abstract

    Software Defined Networking (SDN) is a new networking paradigm that decouples the control logic from the closed and proprietary implementations of traditional network data plane infrastructure. SDN enables researchers to more easily design and distribute innovative flow handling and network control algorithms. We believe that SDN can, in time, prove to be one of the more impactful technologies to drive a variety of innovations in network security. However, to date there remains a stark paucity of SDN security research.

    In this talk, I will discuss some new opportunities as well as challenges in this new research direction, and demonstrate with our recent research results. In the first half of the talk, I will discuss how SDN can enhance network security, e.g., by offering a dramatic simplification to the way we design and integrate complex network security applications/services into large networks. I will introduce our work on FRESCO, a new SDN/OpenFlow security application development framework designed to facilitate the rapid design, and modular composition of SDN-enabled security modules (e.g., for threat detection and mitigation). In the second half of the talk, I will discuss some unique security problems inside SDN, e.g., control plane saturation attacks, and introduce our work on AvantGuard to enhance the robustness and flexibility of SDN.

    What is the Power of Groups?

    Speaker Ram Rajagopal(Stanford University)
    Date 1:30 p.m. November 21st, 2014
    Location GWC 487
    Short Bio
    Ram Rajagopal is an Assistant Professor of Civil and Environmental Engineering and has a courtesy appointment in Electrical Engineering at Stanford University. He directs the Stanford Sustainable Systems Lab (S3L), focused on large scale monitoring, data analytics and stochastic control for energy systems. His current research interests in power systems are in data-driven approaches for the integration of renewables, smart distribution systems and demand-side data analytics. Prior to his current position he was a DSP Research Engineer at National Instruments and a Visiting Research Scientist at IBM Research. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents from his work, and has advised or founded companies in the fields of sensor networks, power systems and data analytics.
    Abstract
    Aggregation of supply and demand-side resources at various scales has been proposed as a mechanism to manage the variability of power networks with significant penetration of renewables. In the supply-side, most studies argue for the formation of large groups of producers that compensate each other's shortfalls. In the demand-side, loads are managed as large aggregates at the unit of neighborhoods or cities. Yet, not much is known based on actual supply, demand and market data. In this talk we investigate the data-driven design of right-sized groups. In the supply-side, we formulate a Cournot competition game based on system operator market data. The resulting model demonstrates that efficient coalitions have an optimal size. In the demand-side we investigate optimal pricing for consumers utilizing hourly smart meter data from 500,000 households. We propose a simple and scalable revenue management mechanism that shows effective pricing divides customers into groups of optimal size. We conclude the talk by highlighting additional important features from data and outline some ongoing and future work. This presentation is based on joint work with Ramesh Johari, Jungsuk Kwac, Sid Patel, Raffi Sevlian and Baosen Zhang.

    The Cost of Free Spectrum

    Speaker Michael Honig(Northwestern University)
    Date 10:30 a.m. December 17th, 2014
    Location GWC 487
    Short Bio
    Michael L. Honig is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University. He received the B.S. degree in electrical engineering from Stanford University in 1977, and the Ph.D. degrees in electrical engineering from the University of California, Berkeley, in 1981. Prior to joining Northwestern he worked in the Systems Principles Research Division at Bellcore in Morristown, NJ, and at Bell Laboratories in Holmdel, NJ. His recent research has focused on wireless networks, including interference mitigation and resource allocation, and market mechanisms for dynamic spectrum allocation. He is a Fellow of IEEE, the recipient of a Humboldt Research Award for Senior U.S. Scientists, and the co-recipient of the 2002 IEEE Communications Society and Information Theory Society Joint Paper Award and the 2010 IEEE Marconi Prize Paper Award. He is currently a member of the Board of Governors for the IEEE Information Theory Society.
    Abstract
    The explosive growth in demand for mobile broadband data services has motivated regulatory agencies, such as the FCC in the United States, to allocate more spectrum for commercial broadband access. A basic policy decision is whether to assign this new spectrum as licensed for exclusive use, or unlicensed (open access). We first review the policy debate, and subsequently consider a scenario in which new spectrum is added to an existing set of licensed bands. We compare the social welfare obtained from this new spectrum when designated as unlicensed or licensed, accounting for congestion due to interference. We show that adding a small amount of unlicensed spectrum often decreases the total welfare (analogous to Braess's paradox). We also compare the total welfare with a single service provider (monopolist) and many competing providers. When investment cost is taken into account a single service provider can be efficient.

    Mean Field Games: An Approach to Understanding Resource Sharing Systems

    Speaker Srinivas Shakkottai (Texas A&M University)
    Date 2:00 p.m. March 17th, 2015
    Location GWC 487
    Short Bio
    Srinivas Shakkottai received a PhD (2007) in Electrical Engineering, from the University of Illinois at Urbana-Champaign. He was a post-doctoral scholar in Management Science and Engineering at Stanford University in 2007, and is currently an associate professor at the Dept. of ECE at Texas A&M University. Srinivas is the recipient of the Defense Threat Reduction Agency Young Investigator Award (2009) and the NSF Career Award (2012), as well as research awards from Cisco (2008) and Google (2010). He also received an Outstanding Professor Award (2013) and was selected as a TEES Select Young Faculty Fellow (2014) at Texas A&M University.
    Abstract

    We will begin by discussing mean field games as a method of studying systems that have a large number of agents, and where any subset of agents has infrequent interactions. Here, agents model their opponents at any particular interaction through an assumed distribution over their action spaces, and play the best response action against this distribution. We say that the system is at MFE if this best response action turns out to be a sample drawn from the assumed distribution.

    We will discuss several of our recent results in the space of MFE that occur under a repeated game framework in resource sharing networks. Here, there is a set of shared resources, and a mechanism is used during each play to allocate resources to the agents that desire them. The agents might experience positive or negative externalities due to network effects. Examples include online marketplaces, public transportation, smart grids, and P2P networks. In many of these applications, the objective will be to ensure that the achieved MFE is socially desirable.

    We will then present details on a specific application in the context of auction-theoretic scheduling in cellular data networks. In our setting, the agents are smart phone apps that generate service requests, have costs associated with waiting, and bid against each other for service from base stations. The users of the apps spend a geometrically distributed amount of time on each app, and then move on to another. We show that in a system in which we conduct a second-price auction at each base station and schedule the winner at each time, there exists an MFE that will schedule the app with the longest queue at each time. The result suggests that auctions can attain the same socially desirable results as queue-length-based scheduling. We will also present some results on the convergence between a system with a finite number of agents to a mean field case as the number of agents become large.

    Green Multi-Homing Video Transmission in Wireless Heterogeneous Networks

    Speaker Weihua Zhuang (University of Waterloo)
    Date 10:00 a.m, March 18th, 2015
    Location GWC 487
    Short Bio
    Weihua Zhuang (M’93-SM’01-F’08) has been with the Department of Electrical and Computer Engineering, University of Waterloo, Canada, since 1993, where she is a Professor and a Tier I Canada Research Chair in Wireless Communication Networks. Her current research focuses on resource allocation and QoS provisioning in wireless networks, and on smart grid. She is a co-recipient of several best paper awards from IEEE conferences. Dr. Zhuang was the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2007-2013), and the Technical Program Symposia Chair of the IEEE Globecom 2011. She is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada, and an elected member in the Board of Governors and VP Mobile Radio of the IEEE Vehicular Technology Society. She was an IEEE Communications Society Distinguished Lecturer (2008-2011).
    Abstract
    The wireless communication medium has become a heterogeneous environment with various wireless access options and overlapped coverage from different networks. Mobile terminals (MTs), equipped with multi-homing capabilities, can explore network cooperation to simultaneously aggregate the offered resources from different networks to support the same application and thus increase the data rate. On the other hand, as the gap between the MT energy demand and battery capacity continues to increase, the MT operational time in between battery charging has become a significant factor in service quality. In this presentation, we introduce an energy management system for MTs to support a sustainable multi-homing video transmission, over the call duration, in a heterogeneous wireless access medium. Through statistical video quality guarantee, the MT can determine a target video quality lower bound for a target call duration. The target video quality lower bound captures the MT available energy at the beginning of the call, the time varying bandwidth availability and channel conditions at different radio interfaces, the target call duration, and the video packet characteristics in terms of distortion impact, delay deadlines, and video packet encoding statistics. The MT then adapts its energy consumption to support at least the target video quality lower bound during the call. Simulation results demonstrate the superior performance of the proposed framework over two benchmarks, and some performance trade-offs.

    ITLinQ: A New Approach for Spectrum Sharing in Device-to-Device Communication Systems

    Speaker Salman Avestimehr (University of Southern California)
    Date 1:30 p.m., April 10th, 2015
    Location GWC 487
    Short Bio

    Salman Avestimehr is an Associate Professor at the Electrical Engineering Department of University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science, both from the University of California, Berkeley. Prior to that, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003. He was an Assistant Professor at the ECE school of Cornell University from 2009 to 2013. He was also a postdoctoral scholar at the Center for the Mathematics of Information (CMI) at Caltech in 2008. His research interests include information theory, the theory of communications, and their applications.

    Dr. Avestimehr has received a number of awards, including the Communications Society and Information Theory Society Joint Paper Award in 2013, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2011 for "pushing the frontiers of information theory through its extension to complex wireless information networks", the Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research in 2011, the National Science Foundation CAREER award in 2010, and the David J. Sakrison Memorial Prize in 2008. He has been a Guest Associate Editor for the IEEE Transactions on Information Theory Special Issue on Interference Networks and General Co-Chairs of the 2012 North America Information Theory Summer School and the 2012 Workshop on Interference Networks. He is currently an Associate Editor for the IEEE Transactions on Information Theory.
    Abstract
    We consider the problem of spectrum sharing in device-to-device communication systems. We define a new concept of information-theoretic independent sets (ITIS), which indicates the sets of users for which simultaneous communication and treating the interference from each other as noise is information-theoretically optimal (to within a constant gap). Based on this concept, we develop a new spectrum sharing mechanism, called information-theoretic link scheduling (ITLinQ), which at each time schedules those users that form an ITIS. We demonstrate that ITLinQ can outperform similar state-of-the-art spectrum sharing mechanisms, such as FlashLinQ, by more than a 100% of sum-rate gain, while keeping the complexity at the same level.

    Signal Processing and Communication Challenges for the Internet of Energy

    Speaker Anna Scaglione(Arizona State University)
    Date 1:30 p.m. May 1st, 2015
    Location GWC 487
    Short Bio

    Anna Scaglione received her M.Sc. and Ph.D. degrees in Electrical Engineering from the University of Rome “La Sapienza”, Italy, in 1995 and 1999,respectively. She was Professor of Electrical Engineering previously at UC Davis (2010–2014), Associate Professor at UC Davis 2008–2010 and at Cornell (2006–2008), and Assistant Professor at Cornell (2001–2006) and at the University of New Mexico (2000–2001). She is currently a professor in electrical and computer engineering at Arizona State University.

    Dr. Scaglione’s expertise is in the broad area of statistical signal processing for communication, electric power systems and networks. Her current research focuses on studying and enabling decentralized learning and signal processing in networks of sensors.

    Dr. Scaglione was elected an IEEE fellow in 2011. She served as Associate Editor for the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS and IEEE TRANSACTIONS ON SIGNAL PROCESSING, as Editor-in-Chief of the IEEE SIGNAL PROCESSING LETTERS. She was member of the Signal Processing Society Board of Governors from 2011 to 2014. She received the 2000 IEEE Signal Processing Transactions Best Paper Award and more recently was honored for the 2013 IEEE Donald G. Fink Prize Paper Award for the best review paper in that year in IEEE publications, her work with her student earned 2013 IEEE Signal Processing Society Young Author Best Paper Award (Lin Li).
    Abstract
    In this talk we will discuss signal processing models of the behavior of electric appliances that can support the smart electric power grid. An ecosystems of Electric Vehicles, Smart Thermostats and Smart Lighting will allow customers to interact with the market of electricity directly, optimizing the customer preferences while better exploiting the variable production from renewable energy, from distributed ``prosumers” and centralized plants alike. The opportunities for good are immense but there are several challenges. Unlike the internet, which is managed in a decentralized fashion, power systems are large vertically integrated infrastructures and, thus, the interaction between market forces is hampered by the curse of dimensionality. We will discuss the issue of sifting through big data to decide the schedule and closing the loop on a large number of transactions. If time allows we will touch upon the issue of cyber-security and privacy that arise in general with the Internet of Things and with the Internet of Energy in particular.

    Privacy against inference attacks: From Theory to Practice

    Speaker Nadia Fawaz (Technicolor Research Center in Los Altos, CA)
    Date 10:30 a.m., August 21st, 2015
    Location GWC 487
    Short Bio
    Nadia Fawaz is a principal research scientist at Technicolor research center in Los Altos, CA. Her current research interests include data privacy and personalization. Her work leverages techniques from information theory, random matrix theory, statistics and privacy theory, and aims at bridging theory and practice. From 2009 to 2011, she was a postdoctoral researcher in the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT), Cambridge, MA. She received her Ph.D. degree in 2008 and her Diplôme d’ingénieur (M.Sc.) in 2005 both in Electrical Engineering, from École Nationale Supérieure des Télécommunications de Paris and EURECOM, France. She is a Member of IEEE and of ACM.
    Abstract

    We propose a general statistical inference framework to capture the privacy threat under inference attacks, i.e. the threat incurred by a user who wishes to release data that is correlated with his private data to a service provider, in the hope of getting some utility. Under this framework, data is distorted before it is released, according to a utility-aware probabilistic privacy mapping. By introducing the log-loss in the privacy metric, we show that the privacy-utility tradeoff can be characterized by a non-asymptotic information-theoretic formulation. The resulting design problem of finding the optimal privacy mapping from the user's data to a privacy-preserving output can be formulated as a convex optimization.

    We then focus on practical challenges encountered when applying this framework to real world data. On one hand, the design of optimal privacy mappings requires knowledge of the prior distribution linking private data and data to be released, which is often unavailable in practice. On the other hand, the optimization may become intractable and face scalability issues when data assumes values in large size alphabets, is high dimensional, or is dynamic. Our work makes the following contributions. First, we provide bounds on the impact on the privacy-utility tradeoff of a mismatched prior. Second, we show how to reduce the optimization size by introducing a quantization step. Third, we evaluate our methods on several datasets, and demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g. recommendations.

    Finally, we demonstrate PriView, an interactive privacy-preserving personalized video consumption system, that protects a user’s privacy while delivering relevant content recommendations to the user. PriView provides the user with three functionalities: transparency on privacy risk, control of privacy risk, and personalized content recommendations.

    Cloud Storage Space vs. Download Time for Large Files

    Speaker Emina Soljanin (Bell Labs, Murray Hill, NJ)
    Date 1:30 p.m., ​September 1​1th, 201​5​
    Location GWC 487
    Short Bio
    Emina Soljanin is a Distinguished Member of Technical Staff at Bell Labs. Her interests and expertise are wide. Over the past quarter of the century, she has participated in numerous research and business projects, as diverse as power system optimization, magnetic recording, color space quantization, hybrid ARQ, network coding, data and network security, and quantum networking. Dr. Soljanin served as the Associate Editor for Coding Techniques, for the IEEE Transactions on Information Theory, on the Information Theory Society Board of Governors, and in various roles on other journal editorial boards and conference program committees. She is a co-organizer of the DIMACS 2001-2005 Special Focus on Computational Information Theory and Coding and 2011-2015 Special Focus on Cybersecurity. Dr. Soljanin has mentored many summer interns, Ph.D. students, and postdocs, and have co-authored two monographs on network coding, which are widely used for classroom teaching and independent studies.
    Abstract
    Users of cloud systems demand that their data be reliably stored and quickly accessible. Cloud providers today strive to meet these demands through over-provisioning: keeping processors ready to go at all times and replicating data over multiple servers. Special erasure codes have been designed and adopted in practice as a more storage-efficient way to provide reliability. We will show how coding reduces download time of large files, in addition to providing reliability against disk failures. For the same total storage used, coding exploits the diversity and parallelism in the system better than today's replication schemes, and hence gives faster download. We will introduce a fork-join queuing framework to model multiple users requesting their data simultaneously, and demonstrate the trade-off between the download time and the amount of storage space. At the end, we will mention several problems that arise in distributed systems when the stored data is large, changing, and expanding.

    Compressed Sensing and High-Resolution Image Inversion: Cautionary Notes

    Speaker Ali Pezeshki (Colorado State University)
    Date 1:30 p.m., October 9th, 201​5​
    Location GWC 487
    Short Bio
    Ali Pezeshki received the B.Sc. and M.Sc. degrees in electrical engineering from University of Tehran, Tehran, Iran, in 1999 and 2001, respectively. He received his PhD degree in electrical engineering at Colorado State University in 2004. In 2005, he was a postdoctoral research associate with the Electrical and Computer Engineering Department at Colorado State University. From January 2006 to August 2008, he was a postdoctoral research associate with The Program in Applied and Computational Mathematics at Princeton University. In August 2008, he joined the faculty of Colorado State University, where he is now an Associate Professor in the Department of Electrical and Computer Engineering, and the Department of Mathematics. His research interests are in statistical signal processing, coding theory, applied harmonic analysis, and bioimaging. He has been serving as a member of the editorial board of IEEE Access since 2012.
    Abstract

    Broadly speaking there are two classical principles for inverting the kinds of images that are measured in optics, electromagnetics and acoustics. The first principle is one of matched filtering, wherein a sequence of rank-one subspaces, or one-dimensional test images, is matched to the measured image by filtering, correlating, or phasing in frequency, wavenumber, doppler, and/or delay. The second principle is one of parameter estimation in a separable linear model, wherein a sparse modal representation for the field is posited and estimates of linear parameters (complex amplitudes of modes) and nonlinear mode parameters (frequency, wavenumber, delay, and/or doppler) are extracted, usually based on maximum likelihood, or some variation on linear prediction. An important limitation of the classical principles is that any subsampling of the measured image has consequences for resolution (or bias) and for variability (or variance).

    Compressed sensing theory stands in contrast to the classical principles. It states that complex baseband data may be compressed before processing, when it is known a priori that the field to be imaged is sparse in a known dictionary, and it suggests that subsampling has manageable consequences for image inversion. Moreover, the compression step in compressed sensing typically employs randomly drawn linear combinations, which stand in stark contrast to the linearly phased combinations that are used to form narrow bands in time series analysis and focused beams in space series analysis.

    But how does the performance of compressed sensing compare with that of classical and modern methods of modal analysis, such as matched filters, generalized sidelobe cancelers, MUSIC, linear prediction, and maximum likelihood approximations? This is the general question that we discuss in this talk. More specifically, we discuss recent analytical and experimental results that answer the following fundamental questions:

    1. Sensitivity to model mismatch: What is the sensitivity of compressed sensing to mismatch between the physical model that generated the data and the mathematical model that is assumed in the inversion algorithm? Can these sensitivities be mitigated by over resolving the mathematical model to ensure that mathematical modes are close to physical modes? For inversion problems where mismatch turns sparse problems into incompressible problems, can the imaging system or the inversion algorithm be modified to restore compressibility?

    2. Loss of Fisher Information: What is the impact of compressive sampling on the Fisher information matrix, Cramer-Rao bound (CRB), and Kullback-Leibler divergence for estimating nonlinear parameters? How well does a compressively recorded version of a space-time image preserve information about the field of scatterers to be estimated?

    3. Threshold effects and performance breakdowns: What is the impact of compressive sampling on SNR thresholds at which mean-squared error (MSE) in estimating parameters deviate sharply from the CRB? What is the impact of compressive sampling on the probability of a swap of signal and noise subspaces in the data? Can these threshold effects be predicted?

    The results are from joint work with Pooria Pakrooh, Yuejie Chi, Louis Scharf, Doug Cochran, Stephen Howard, Robert Calderbank, and Edwin Chong.

    Distributed Optimization and Learning in Networks

    Speaker Angelia Nedich (University of Illinois at Urbana-Champaign)
    Date 1:30 p.m., October 16th, 2015
    Location GWC 487
    Short Bio

    Angelia Nedich received her B.S. degree from the University of Montenegro (1987) and M.S. degree from the University of Belgrade (1990), both in Mathematics. She received her Ph.D. degrees from Moscow State University (1994) in Mathematics and Mathematical Physics, and from Massachusetts Institute of Technology in Electrical Engineering and Computer Science (2002). She has been at the BAE Systems Advanced Information Technology from 2002-2006. In Fall 2006, she has joined the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, USA.  She is a recipient of the NSF CAREER Award 2007 in Operations Research for her work in distributed multi-agent optimization. She has received the Donald Biggar Willett Scholar of Engineering award in 2013 and the Dean’s Award for Excellence in Research in 2015, both awarded by the College of Engineering at the University of Illinois at Urbana-Champaign.

    Her general interest is in optimization and dynamics including fundamental theory, models, algorithms, and applications. Her current research interest is focused on large-scale convex optimization, distributed multi-agent optimization and equilibrium problems, stochastic approximations, and network aggregation-dynamics with applications in signal processing, machine learning, and decentralized control.
    Abstract
    We consider a system of networked agents who cooperate in order to perform some global network task such as optimization, learning, detection or estimation. In this model each agent has access to its own convex function and other local information and the collective goal is to minimize the sum of their functions. The local objective functions capture the task that an agent needs to perform. The communications between nodes are described by a time-varying sequence of directed graphs, which is uniformly strongly connected. For such communications, assuming that every node knows its out-degree, we develop a broadcast-based algorithm, termed the (sub)gradient-push, which steers every node to an optimal value of the global network problem. We also discuss the results when the algorithm is specialized to a distributed hypothesis testing problem.

    Data-driven Information Divergence Measures

    Speaker Visar Berisha (Arizona State University)
    Date 1:30 p.m., October 23rd, 2015
    Location GWC 487
    Short Bio
    Visar Berisha received his PhD from Arizona State University in 2007. From 2007 to 2009, he was a member of the technical staff at the Massachusetts Institute of Technology - Lincoln Laboratory. Following his appointment at Lincoln Labs, Dr. Berisha was Principal Engineer at Raytheon Co. Since 2013, Dr. Berisha has been an assistant professor at Arizona State University with a joint appointment in the School of Electrical Computer and Energy Engineering and the Department of Speech and Hearing Sciences. His current research interests include computational models of speech and audio perception, pathological speech processing, and statistical signal processing.
    Abstract
    Information divergence functions play a critical role in statistics and information theory. In this presentation we first present a new f-divergence measure can be used to (1) provide improved bounds on the minimum classification probability of error and (2) estimate the Fisher Information Matrix for complex systems that cannot be described analytically. Furthermore this divergence measure can be estimated directly from data - we present an asymptotically consistent estimator of the divergence measure that does not require density estimates of the two distributions. Following, we also outline a formulaic design process for how to construct data-driven estimators for existing divergence functions and how to design your own divergence with custom properties. Throughout the presentation we will complement the theoretical results with empirical results from various speech classification and estimation tasks.

    Enhancing STEM Education Through Robotics Outreach - A Pipeline Approach

    Speaker Tanja Karp (Texas Tech University)
    Date 1:30 p.m., November 23rd, 201​5​
    Location GWC 487
    Short Bio

    Dr. Tanja Karp is an Associate Professor of Electrical and Computer Engineering at Texas Tech University (TTU) in Lubbock, Texas. Prior to joining the faculty at Texas Tech as an Assistant Professor in 2000, she was a Senior Research and Teaching Associate at the Institute of Computer Engineering at Mannheim University, Germany. Dr. Karp received her PhD and MSEE degrees in Electrical Engineering from Hamburg University of Technology, Germany, in 1993 and 1997, respectively.

    Dr. Karp has published over 80 journal and conference articles in the fields of digital signal processing, multicarrier communications, K-12 robotics and STEM education. She annually holds robotics workshops at K-12 teacher and engineering education conferences. During the last 10 years she has been involved in K-12 engineering outreach geared at attracting more and better qualified students into engineering careers and increasing the retention of engineering undergraduate students.

    Dr. Karp was the recipient of the 2012 IEEE/Hewlett Packard Harriett B. Rigas Award which recognizes an outstanding woman engineering educator in electrical and computer engineering. In 2015 she was recognized as one of 100 Inspiring Women in STEM by INSIGHT Into Diversity Magazine. She received several teaching awards from Texas Tech University including the President’s Excellence in Teaching Award (2015), the Butler Distinguished Educator Award (2012-2014), the Lockheed Martin Aeronautics Company Excellence in Engineering Teaching Award (2003 & 2009), the Spencer A. Wells Creativity in Teaching Award (2006), the George T. & Gladys Abell Hanger Faculty Teaching Award (2006). Dr. Karp was named a 2014 TTU Integrated Scholar in recognition of her integration of teaching, research, and service. She is a TTU Service Learning Fellow and was introduced into the TTU Teaching Academy in 2011.
    Abstract
    The Whitacre College of Engineering has implemented a pipeline of robotics challenges for K-12 students during the last 10 years. While competitions are geared toward primary and secondary school participants, undergraduate engineering students play an active role in them as mentors and volunteers. In this presentation we first provide an overview of programs offered and our reasons to pick those. Next, we will present the various ways undergraduate students benefit from these K-12 robotics programs. Finally, we will discuss successes and challenges we have experienced during the last decade.

    Using Software-Defined Networking to Radically Simplify and Harden Enterprise Networks

    Speaker Bryan Larish (National Security Agency)
    Date 11:00 a.m., November 30th, 2015
    Location GWC 487
    Short Bio

    As Technical Director for Enterprise Connectivity & Specialized IT Services at the National Security Agency (NSA), Bryan Larish is responsible for setting the technical direction of the development and operation of NSA's global network infrastructure.

    Prior to joining NSA, Bryan worked in the Chief Engineer's office at the U.S. Navy's Space and Naval Warfare Systems Command (SPAWAR). In that role, he was responsible for implementing engineering techniques used to manage, architect, and plan the U.S. Navy's communications/IT systems portfolio. Bryan's other experience includes Technical Director for Navy engineering policy and various engineering roles at SPAWAR.

    Bryan holds a Ph.D. and M.S. in electrical and computer engineering from the Georgia Institute of Technology and a B.S.E. in electrical engineering from Arizona State University.
    Abstract
    Recent security breaches at large organizations across a variety of industries (e.g., federal, health care, financial) have highlighted the importance of network security. In this talk we start with a fundamental hypothesis - that any enterprise wishing to operate a secure network must be able to characterize everything in and connected to the network - and show how software-defined networking (SDN) technologies can be used to implement a network with this property. We then leverage SDN capabilities to fundamentally change how the network operates. The result is an enterprise network design that significantly more simple and secure than a traditional network.

    A Unified Framework for Large-Scale Block-Structured Optimization

    Speaker Mingyi Hong (Iowa State University)
    Date 1:30 p.m., February 26th, 2016
    Location GWC 487
    Short Bio
    Mingyi Hong received the B.E. degree from Zhejiang University, China, the M.S. degree in Stony Brook University, and the Ph.D. degree from University of Virginia in 2005, 2007, and 2011 respectively. From 2011 to 2014 he holds research positions in the Department of Electrical and Computer Engineering, University of Minnesota. He is currently a Black & Veatch Faculty Fellow and an Assistant Professor with the Department of Industrial and Manufacturing Systems Engineering and the Department of Electrical and Computer Engineering (by courtesy), Iowa State University. His research interests are primarily in the fields of large-scale optimization theory, statistical signal processing, next generation wireless communications, and their applications in big data problems.
    Abstract
    In this talk we present a powerful algorithmic framework for large-scale optimization, called the Block Successive Upper bound Minimization (BSUM). The BSUM includes as special cases many well-known methods for signal processing, communication or massive data analysis, such as Block Coordinate Descent (BCD), Convex-Concave Procedure (CCCP), Block Coordinate Proximal Gradient (BCPG) method, Nonnegative Matrix Factorization (NMF), Expectation Maximization (EM) method and so on. In this talk, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency and parallel/distributed implementation. Illustrative examples from networking, signal processing and machine learning are presented to demonstrate the practical performance of the BSUM framework.

    Stochastic and Information-theoretic Approaches to Analysis and Storage of Biological Data

    Speaker Farzad Farnoud (California Institute of Technology)
    Date 1:30 p.m., March 11th, 2016
    Location GWC 487
    Short Bio
    Farzad Farnoud is a postdoctoral scholar at the California Institute of Technology. He received his MS degree in Electrical and Computer Engineering from the University of Toronto in 2008. From the University of Illinois at Urbana-Champaign, he received his MS degree in mathematics and his PhD in Electrical and Computer Engineering in 2012 and 2013, respectively. His research interests include the information-theoretic and probabilistic analysis of genomic evolutionary processes; rank aggregation and gene prioritization; and coding for flash memory and DNA storage. He is a recipient of the Robert T. Chien Memorial Award for demonstrating excellence in research in electrical engineering from the University of Illinois at Urbana-Champaign.
    Abstract
    By 2025, we may be generating as much as 1 Zetta bases of DNA sequencing data per year, with its growth potentially outpacing computational power and storage capacity. It is thus imperative to develop efficient analysis and storage algorithms in order to benefit from the full potential of available biological data. In this talk, I will present our work on aspects of both analysis and storage of such data. First, I will describe a method for estimating the rates of tandem duplication and substitution mutations in DNA tandem repeat regions, which form about 3% of the human genome and are known to cause several diseases. The proposed method, obtained through a stochastic approximation framework, presents an efficient alternative to solving the possibly NP-hard problem of combinatorially reconstructing duplication histories. We show that compared to previous algorithms, this method achieves better accuracy while being more scalable. The mutation rate estimates can be used to approximate distances between genomic sequences for phylogenetic reconstruction; and also for capacity computation and design of error-correcting codes for DNA data embedding. In the context of storage of biological data, I will present MetaCRAM, our compression platform for metagenomic sequence reads, which aims to address challenges arising from the growing size of metagenomic datasets, by integrating taxonomy identification, alignment, and source coding. By testing MetaCRAM on a variety of metagenomic datasets, we show that it reduces file sizes by more than 87%, outperforming standard tools.

    Minimizing Latency in Cloud Based Systems: Replication Over Parallel Servers

    Speaker Yin Sun (Ohio State University)
    Date 3:30 p.m., April 7th, 2016
    Location GWC 487
    Short Bio
    Yin Sun received his B.S. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2006 and 2011, respectively, both in Electrical Engineering. He received the Excellent Doctoral Dissertation Award of Tsinghua University, Excellent Bachelor's Thesis Award of Tsinghua University, and many scholarships. Since 2014, Yin Sun has been a research associate in the Department of Electrical and Computer Engineering at the Ohio State University. He was a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at the Ohio State University during 2011-2014. His research interests include the fundamental limits in the design, control, performance of information and computer systems, with applications to large-scale Web services, cyber-physical systems, and communication networks. The paper he co-authored received the best student paper award at IEEE WiOpt 2013.
    Abstract
    We are in the midst of a major data revolution. The total data generated by humans from the dawn of civilization until the turn of the new millennium is now being generated every two days. Driven by a wide range of data-intensive devices and applications, this growth is expected to continue its astonishing march, and fuel the development of new and larger data centers. In order to exploit the low-cost services offered by these resource-rich data centers, application developers are pushing computing and storage away from the end-devices and instead deeper into the data-centers. Hence, the end-users' experience is now dependent on the performance of the algorithms used in data-centers. In particular, providing low-latency services is critically important to the end-user experience for a wide variety of applications. Our goal has been to develop the analytical foundations and methodologies to enable cloud computing and storage solutions that result in low-latency services. A variety of of cloud based systems can be modeled using multi-server, multi-queue queueing systems with data locality constraints. In these systems, replication (or most sophisticated coding schemes) can be used to not only improve reliability but to also reduce latency. However, delay optimality for multi-server queueing systems has been a long-standing open problem, with limited results usually in asymptotic regimes. The key question is can we design resource allocation schemes that are near optimal in distribution for minimizing several different classes of delay metrics that are important in web and cloud based services? In this talk, I will overview some of our recent research efforts at solving this problem, provide some key design principles, and outline a set of what I believe are important open problems.

    SGD and Randomized Projections Methods for Linear Systems

    Speaker Deanna Needell (Claremont McKenna College)
    Date 1:30 p.m., April 8th, 2016
    Location GWC 487
    Short Bio
    Deanna Needell earned her PhD from UC Davis under the advisement of Roman Vershynin in Topics in Compressed Sensing. She then did a two year postdoc at Stanford with Emmanuel Candès and is currently an associate professor at Claremont McKenna College in Southern California. She has received awards including the IEEE Best Paper Award, an Alfred P. Sloan fellowship, and an NSF CAREER award.
    Abstract
    In this talk we will give a brief overview of stochastic gradient pursuit and the closely related Kaczmarz method for solving linear systems, or more generally convex optimization problems. We will present some new results which tie these methods together and prove the best known convergence rates for these methods under mild Lipschitz conditions. The methods empirically and theoretically rely on probability distributions to dictate the order of sampling in the algorithms. It turns out that the choice of distribution may drastically change the performance of the algorithm, and the theory has only begun to explain this phenomenon.

    Upcoming Seminar: Modeling and Optimizing Complex Dynamic Transportation Systems: A State-space-time Network-based Framework

    Speaker Xuesong Zhou (Arizona State University)
    Date 1:30 p.m., April 15th, 2016
    Location GWC 487
    Short Bio
    Dr. Xuesong Zhou is an Associate Professor at the School of Sustainable Engineering and the Built Environment at Arizona State University. Dr. Zhou’s research interests include large-scale dynamic transportation routing assignment, simulation, and optimization. Dr. Zhou is currently an Associate Editor of Transportation Research Part C, an Associate Executive Editor-in-Chief of Urban Rail Transit, an Associate Editor of Networks and Spatial Economics, an Editorial Board Member of Transportation Research Part B. He is Chair of INFORMS Rail Application Section, and the Co-Chair of the IEEE ITS Society Technical Commit¬tee on Traffic and Travel Management, as well as a subcommittee chair of the TRB Committee on Transportation Network Modeling (ADB30). He is the principle architect and developer of DTALite, a light-weight open-source traffic assignment/simulation engine, and he has been assisting FHWA, many state DOT and metropolitan planning agencies to learn and deploy advanced transportation network modeling tools.
    Abstract
    Transportation state estimation and optimization techniques aim to use accurate state representation and optimized decisions to guide planning and operational management decisions. A wide range of time-discretized network flow models have been proposed to represent transportation systems through space-time or time-expanded networks. By adding additional state dimensions (e.g., energy, speed and vehicle carrying states), we are able to construct a systematic representation to prebuild many complex state transition constraints into a well-structured hyper network, so that the resulting optimization model can be nicely reformulated as multi-commodity network flow models with a very limited number of side constraints. In this talk, we will walk through examples of recasting several classic transportation systems optimization problems using the SST framework, namely solving large-scale vehicle ridesharing optimization, electronic vehicle routing, and signal phase optimization.

    The High-Dimensional Limit of Stochastic Iterative Methods for Convex and Nonconvex Optimization: Dynamics and Phase Transitions

    Speaker Yue M. Lu (Harvard University)
    Date 10:30 a.m., Sept 27th, 2016
    Location GWC 487
    Short Bio
    Yue M. Lu was born in Shanghai. After finishing undergraduate studies at Shanghai Jiao Tong University, he attended the University of Illinois at Urbana-Champaign, where he received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering, both in 2007. He was a Research Assistant at the University of Illinois at Urbana-Champaign, and has worked for Microsoft Research Asia, Beijing, and Siemens Corporate Research, Princeton, NJ. Following his work as a postdoctoral researcher at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, he joined Harvard University in 2010, where he is currently an Associate Professor of Electrical Engineering at the John A. Paulson School of Engineering and Applied Sciences.

    He received the Most Innovative Paper Award (with Minh N. Do) of IEEE International Conference on Image Processing (ICIP) in 2006, the Best Student Paper Award of IEEE ICIP in 2007, and the Best Student Presentation Award at the 31st SIAM SEAS Conference in 2007. Student papers supervised and coauthored by him won the Best Student Paper Award (with Ivan Dokmanic and Martin Vetterli) of IEEE International Conference on Acoustics, Speech and Signal Processing in 2011 and the Best Student Paper Award (with Ameya Agaskar and Chuang Wang) of IEEE Global Conference on Signal and Information Processing (GlobalSIP) in 2014.

    He has been an Associate Editor of the IEEE Transactions on Image Processing since 2014, an Elected Member of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee since 2015, and an Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee since 2016. He received the ECE Illinois Young Alumni Achievement Award in 2015.
    Abstract
    We consider efficient iterative methods (e.g., stochastic gradient descent, randomized Kaczmarz algorithms, iterative coordinate descent) for solving large-scale optimization problems, whether convex or nonconvex. A flurry of recent work has focused on establishing their theoretical performance guarantees. This intense interest is spurred on by the remarkably impressive empirical performance achieved by these low-complexity and memory-efficient methods.

    In this talk, we will present a framework for analyzing the exact dynamics of these methods in the high-dimensional limit. For concreteness, we consider two classical problems: regularized linear regression (e.g. LASSO) and sparse principal component analysis. For each case, we show that the time-varying estimates given by the algorithms will converge weakly to a deterministic “limiting process” in the high-dimensional (scaling and mean-field) limit. Moreover, this limiting process can be characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithms. For example, performance metrics such as the MSE, the cosine similarity and the misclassification rate in sparse support recovery can all be obtained by examining the deterministic limiting process. A steady-state analysis of the nonlinear PDE also reveals interesting phase transition phenomenons related to the performance of the algorithms. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions.

    What makes our analysis tractable is the notion of exchangeability, a fundamental property of symmetry that is inherent in many of the optimization problems encountered in signal processing and machine learning.

    Delay-Optimal Scheduling for Data Center Networks and Input-Queued Switches in Heavy Traffic

    Speaker Siva Theja Maguluri (IBM)
    Date 1:30 p.m., Oct 28th, 2016
    Location GWC 487
    Short Bio
    Siva Theja Maguluri is a Research Staff Member in the Mathematical Sciences Department at IBM T. J. Watson Research Center. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in Electrical and Computer Engineering where he worked on resource allocation algorithms for cloud computing and wireless networks. Earlier, he received an MS in ECE and an MS in Applied Math from UIUC and a B.Tech in Electrical Engineering from IIT Madras. His research interests include Stochastic Processes, Optimization, Cloud Computing, Data Centers, Resource Allocation and Scheduling Algorithms, Networks, and Game Theory. He will start next spring as an Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech.
    Abstract
    Today's era of cloud computing is powered by massive data centers. A data center network enables the exchange of data in the form of packets among the servers within these data centers. Given the size of today's data centers, it is desirable to design low-complexity scheduling algorithms which result in a fixed average packet delay, independent of the size of the data center. We consider the scheduling problem in an input-queued switch, which is a good abstraction for a data center network. In particular, we study the queue length (equivalently, delay) behavior under the so-called MaxWeight scheduling algorithm, which has low computational complexity. Under various traffic patterns, we show that the algorithm achieves optimal scaling of the heavy-traffic scaled queue length with respect to the size of the switch. This settles one version of an open conjecture that has been a central question in the area of stochastic networks. We obtain this result by using a Lyapunov-type drift technique to characterize the heavy-traffic behavior of the expected total queue length in the network, in steady-state.

    Subspace Detection with Applications

    Speaker Louis Scharf (Colorado State University)
    Date 1:30 p.m., Jan 26, 2017
    Location GWC 487
    Short Bio
    Louis Scharf received his Ph.D. from the University of Washington, Seattle. From 1971 to 1982 he served as Professor of Electrical Engineering and Statistics at CSU. From 1982 to 1985 he was Professor and Chairman of Electrical and Computer Engineering at the University of Rhode Island, Kingston. From 1985 to 2000 he was Professor of Electrical and Computer Engineering at the University of Colorado, Boulder. In January 2001, Professor Scharf rejoins Colorado State University as Professor of Electrical and Computer Engineering, and Statistics.

    Professor Scharf has held several visiting positions here and abroad. He has developed particularly close ties with Ecole Superieure d'Electricite (Gif-sur-Yvette), Ecole Nationale Superieure des Telecommunications (Paris), and EURECOM (Nice). He is a recognized expert in statistical signal processing, as it applies to adaptive radar, sonar, and wireless communication. His most important contributions to date are to invariance theories for detection and estimation; matched and adaptive subspace detectors for radar, sonar, and data communication; and canonical decompositions for reduced dimensional filtering and quantizing. His current interests are in rapidly-adaptive receiver design for space-time signal processing in the wireless communication channel.

    Professor Scharf is a Fellow of IEEE. He chairs the Fellow Committee for the IEEE Signal Processing Society, and serves on its Technical Committees for Theory and Methods and for Sensor Arrays and Multichannel Signal Processing. He has received numerous awards for his research contributions to statistical signal processing, including an IEEE Distinguished Lectureship, an IEEE Third Millenium Medal, and the Technical Achievement Award from the IEEE Signal Processing Society.
    Abstract
    (To be Updated)

    An informational perspective on uncertainty in control

    Speaker Gireeja Ranade (Microsoft Research, Redmond)
    Date 1:30 p.m., Feb 27, 2017
    Location GWC 409
    Short Bio
    Gireeja Ranade is a postdoctoral researcher at Microsoft Research, Redmond. Before this she was a lecturer in EECS at UC Berkeley working on designing and teaching the pilot version of novel lower-division EECS classes (16AB). She received an MS and PhD in EECS from UC Berkeley and an SB in EECS from MIT. She has worked on topics in brain-machine interfaces, information theory, control theory, wireless communications and crowdsourcing.
    Abstract
    Developing high-performance cyber-physical systems requires a deep understanding of how uncertainty and unpredictability impair performance. In this talk, I discuss some theoretical perspectives to understand uncertainty in systems as well as practical protocols to mitigate it. I will first introduce a notion of "control capacity," which parallels the notion of Shannon communication capacity, and provides a fundamental limit on the ability to stabilize a system with random time-varying parameters (modeled as multiplicative noise). Further, it can be used to quantify the value of side-information in control. We contrast systems with noisy actuation (e.g., when motors on a drone cannot precisely execute control actions) to noisy sensing (e.g., miscalibrated cameras). In the first case, we show that linear control strategies are optimal, while in the second, we show that non-linear strategies can outperform them. Further, we use techniques from information-theory and probability-theory to bound the improvement that non-linear strategies can bring. Finally, I will shift from quantifying the effect of uncertainty to methods for reducing uncertainty. With the aim of enabling industrial automation, I will discuss the development of highly-reliable low-latency wireless communication protocols for machine-to-machine communication. The talk will include joint work with Jian Ding, Yuval Peres, Govind Ramnarayan, Anant Sahai, Sahaana Suri, Vasuki Narasimha Swamy, and Alex Zhai.

    Orthogonal precoding for sidelobe suppression in DFT-based systems using block reflectors

    Speaker Vaughan Clarkson (University of Queensland)
    Date 1:30 p.m., Mar 3, 2017
    Location GWC 487
    Short Bio
    Education:
    Bachelor of Science (Mathematics), UQ, 1989
    Bachelor of Engineering (Computer Systems; Hons I), UQ, 1990
    Doctor of Philosophy, ANU, 1997
    Abstract
    Sidelobe suppression has always been an important part of crafting communications signals to keep interference with users of adjacent spectrum to a minimum. Systems based on the discrete Fourier transform, such as orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-division multiple access (SC-FDMA) are especially prone to out-of-band power leakage. Although many techniques have been proposed to suppress sidelobes in DFT-based systems, a satisfactory balance between computational complexity and out-of-band power leakage has remained elusive.

    Orthogonal precoding is a promising, linear technique in which the nullspace of a precoding matrix with orthonormal columns is designed to suppress the sidelobes. Orthogonal precoders have been proposed that yield excellent out-of-band suppression. However, they suffer from high arithmetic complexity—quadratic in the number of active subcarriers—which has limited their application.

    In this talk, we find that the arithmetic complexity can be made linear instead of quadratic if a block reflector is used to perform the precoding instead of an otherwise unstructured unitary transformation. There is no penalty to be paid in achieved bit-error rate. We show by numerical simulation that the penalty in peak-to-average power ratio is also very small for OFDM.

    On addressing uncertainty and high-dimensionality in optimization and variational inequality problems: self-tuned stepsizes, and randomized block coordinate schemes

    Speaker Farzad Yousefian (Oklahoma State University)
    Date 1:30 p.m., Mar 17, 2017
    Location GWC 487
    Short Bio
    Farzad Yousefian is currently an assistant professor in the school of Industrial Engineering and Management at Oklahoma State University. Before joining OSU, he was a postdoctoral researcher in the Department of Industrial and Manufacturing Engineering at Penn State. He obtained his Ph.D. in industrial engineering from the University of Illinois at Urbana-Champaign in 2013. His thesis is focused on the design, analysis, and implementation of stochastic approximation methods for solving optimization and variational problems in nonsmooth and uncertain regimes. His current research interests lie in the development of efficient algorithms to address ill-posed stochastic optimization and equilibrium problems arising from machine learning and multi-agent systems. He is the recipient of the best theoretical paper award in the 2013 Winter Simulation Conference.
    Abstract
    A wide range of emerging applications in machine learning, signal processing, and multi-agent systems result in optimization, and more generally variational inequality problems. Such models are often complicated by uncertainty, and/or high-dimensionality. In the first part of this talk, we consider stochastic mirror descent methods for solving stochastic convex optimization problems. It has been discussed that the performance of this class of methods is very sensitive to the choice of the stepsize sequence. Motivated by this gap, we present a unifying self-tuned update rule for the stepsize sequence such that: (i) it is characterized in terms of problem parameters and algorithm’s settings; and (ii) under this update rule, a suitably defined error metric is minimized. We present the performance of this update rule for the soft margin linear SVM problem over different large data sets.

    In the second part of the talk, motivated by multi-user optimization problems and non-cooperative Nash games in uncertain regimes, we consider stochastic Cartesian variational inequalities where the number of the component sets is huge. We develop a randomized block stochastic mirror-prox (B-SMP) algorithm, where at each iteration only a randomly selected block coordinate of the solution is updated through implementing two consecutive projection steps. The convergence analysis of the B-SMP method equipped with rate statements will be presented.

    Network interference cancelation

    Speaker Olav Tirkkonen (Aalto University, Finland)
    Date 1:30 p.m., April 6th, 2017
    Location GWC 487
    Short Bio
    Olav Tirkkonen is associate professor in communication theory at the Department of Communications and Networking in Aalto University, Finland, where he has held a faculty position since August 2006. He received his M.Sc. and Ph.D. degrees in theoretical physics from Helsinki University of Technology in 1990 and 1994, respectively. Between 1994 and 1999 he held post-doctoral positions at the University of British Columbia, Vancouver, Canada, and the Nordic Institute for Theoretical Physics, Copenhagen, Denmark. From 1999 to 2010 he was with Nokia Research Center (NRC), Helsinki, Finland. He has published some 200 papers, and is coauthor of the book "Multiantenna transceiver techniques for 3G and beyond". His current research interests are in coding theory, multiantenna techniques, and cognitive management of 5G cellular networks.
    Abstract
    The best known strategies in multiuser interference channels are based on advanced Interference Cancelation (IC) receivers, where the 2-user case have been thoroughly analyzed. Despite this, limited use has been made of network interference cancelation in existing wireless systems. In this talk, the potential of using limited complexity IC receivers in User Equipment (UE) of Device-to-Device (D2D) and cellular networks are discussed. First, the potential of fully distributed IC and Power Control (PC) strategies to provide Radio Resource Management (RRM) in D2D networks are considered. In a game theoretical setting, it is observed that, counterintuitively, a strategic player may voluntarily reduce its transmit power to increase its rate. In a D2D network this leads to low complexity distributed RRM with limited cost of anarchy, as compared to a centralized proportionally fair solution. Next, the potential of using IC at UEs to improve cell-edge performance in a heterogeneous cellular network is addressed. It is shown that applying network IC at UEs breaks the cellular paradigm. With Network IC, some users are best served by their second best cell, using IC against the signal from the strongest cell. This can be used to significantly boost the consistency of user experience over wide area networks.

    Fog Computing and Networking: A New Paradigm for 5G and IoT Services

    Speaker T. Russell Hsing (National Chiao Tung University, Taiwan)
    Date 2:00 p.m., May 23rd, 2017
    Location GWC 487
    Short Bio
    Dr. T. Russell Hsing, a Life Fellow of the IEEE, Fellow of British Computer Society (BCS) in UK, and Fellow of the SPIE-The Internal Society for Optical Engineering. He is now Chair professor of National Chiao Tung University in Hsin-Chu, Taiwan (Aug. 2012 – Present), Visiting Professor for POSTECH in Pohang, Korea (Sep. 1 – Dec. 31 2012), Adjunct Professor with the Chinese Univesity of Hong Kong (since 2015) and Industrial Adviser to the EDGE Lab. of Princeton University (since 2011). Before March, 2012, he has been a Director first (1986-1995) and then Executive Director (1995-March, 2012) to manage and lead the Emerging Technologies and Services Research Department at Telcordia Technologies (formerly Bell core) for 26 years. He was also supervising Directors for the Telcordia Applied Research Center in Poland (TARC-PL) and Taiwan (TARC-TW). Currently he is also an adjunct professor of the Electrical Engineering Department at the Arizona State University, a Member of the Scientific Advisory Board for the Institute of Networks Coding at the Chinese University of Hong Kong, and a visiting professor at the Peking University in China. He is also a member of the IEEE Fellow Committee in 2012, a member for the IEEE Kiyo Tomiyasu Award Committee, and the IEEE Sumner Award Committee for the IEEE; and a member of the Award Committee (2011-2013) for the IEEE Communications Society. He has been a co-Editor-in-Chief of the ICT Book Series for the John Wiley & Sons Publications, Inc. since 2007; and a Founding Editor for the Journal of Visual Communications and Image Representation since 1990. He is now a member for the IEEE Fellow Committee, and a member of the Award Committee for the IEEE Communications Society (since 2011)
    Abstract
    Pushing processing and storage into the “cloud” has been a key trend in networking and distributed systems in the past decade. In the next wave of network architecture and technology advance, the cloud is now descending to be diffused among the client devices, often with mobility too: the cloud is becoming “fog.” For example, more than just faster speed, 5G wireless networks need to be cognitive of end-user application needs. Questions on fairness, robustness, privacy, security, and efficiency need to be revisited. Furthermore, empowered by chips such as Atom and emergent communication protocols, each client device today is powerful in computation, in storage, and in communication. Yet client devices are still limited in battery power, global view of the network, and mobility support. Recognizing the gap between “Cloud” and “Things,” IEEE has stepped up its efforts on filling the “Cloud-to-Thing” continuum through growing its activities in fog computing, communications, storage and control, i.e., “Fog.” Most interestingly, the collection of many Fog-based Networks in a crowd presents a highly distributed, under-organized and dense network.

    The goal of starting the Fog Computing & Networking research is to investigate the optimization of resources that are virtualized, pooled, and shared unpredictably. Fog Networking revisits the role of clients in network architectures, more than just an end-user device, but also as an integral part of the control plane that monitors, measures, and manages the network. This is rewriting the traditional practice of using heavy-duty and dedicated network elements for network measurement and management Fog Computing & Networking combine the study of mobile communications, fog-based radio access network (F-RAN) in 5G, distributed systems, and big data analytics into an exciting new area. Based on our preliminary research, it shows that new emerging services (such as V2V in Vehicular Telematics Services, Industry 4.0 and e-Healthcare Services) could be realized and implemented easily and economically. It could be also served as core engine to enable many Services in Internet of Things (IoT) applications. Both of Future Research Directions and the ICT Convergence for Entrepreneurs in the area of Fog Computing and Networking will be discussed in this talk.
     
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