Speakers at STATOS 2013

Confirmed speakers:

  • Moeness Amim
  • Sergio Barbarossa
  • Yonina Eldar
  • Geert Leus
  • Ken Ma
  • Bjorn Ottersten
  • P.P. Vaidvanathan
  • Alle-Jan van der Veen
  • Watao Yin
  • Georgios Giannakis
  • Nikos Sidiropoulos

Moeness Amim, Villanova University

Title: Compressive sensing for urban radar

Abstract: Compressive Sensing for Urban Radars, or Compressive Urban Sensing (CUS), is an area of research and development which investigates the radar performance within the context of compressive sensing and with a focus on urban applications. CUS examines the effect of using significantly reduced data measurements in time, space and frequency on 2D and 3D imaging quality, strong EM reflections from exterior and interior walls, target ghosts, and moving target detection and tracking. In this respect, CUS is a hybrid between the two areas of compressive sensing and urban sensing. In essence, it enables reliable imaging of indoor targets using a very small percentage of the entire data volume. In this talk, compressive sensing will be put in context for radar, in general, and in particular for the urban environment. We will explain how CS can achieve various radar sensing goals and objectives, and how it compares with the use of full data volume. Different radar specifications and configurations will be used. In particular, we will address CS for urban radars towards achieving (a) Imaging through walls; (b) Detection of behind the wall targets; (c) Mitigation of wall clutter; and (d) Exploitation of multipath. All of the above issues will be examined using data generated at the Radar Imaging Lab, Villanova University.

Bio: Dr. Moeness Amin received his Ph.D. degree in 1984 from University of Colorado, in Electrical Engineering. In 1984. He has been on the Faculty of the Department of Electrical and Computer Engineering at Villanova University since 1985. In 2002, he became the Director of the Center for Advanced Communications, College of Engineering. Dr. Amin is the Recipient of the 2009 Individual Technical Achievement Award from the European Association of Signal Processing, the Recipient of the 2010 NATO Scientific Achievement Award; Recipient of the Chief of Naval Research Challenge Award, 2010; Recipient of the IEEE Third Millennium Medal, 2000; Recipient of Villanova University Outstanding Faculty Research Award, 1997; and the Recipient of the IEEE Philadelphia Section Award, 1997. He is a Fellow of the Institute of Electrical and Electronics Engineers, 2001; Fellow of the International Society of Optical Engineering, 2007; and a Fellow of the Institute of Engineering and Technology, 2010. He was a Distinguished Lecturer of the IEEE Signal Processing Society, 2003-2004. He serves as the Chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts. Dr. Amin has over 600 journal and conference publications in the areas of Wireless Communications, Time-Frequency Analysis, Smart Antennas, Waveform Design and Diversity, Interference Cancellation in Broadband Communication Platforms, Anti-Jam GPS, Target Localization and Tracking, Direction Finding, Channel Diversity and Equalization, Ultrasound Imaging and Radar Signal Processing. He is the Editor of the two books: Through the Wall Radar Imaging (2011) and Compressive Sensing for Urban radar (2014), both by CRC Press.

This talk is sponsored by the European Association for Signal Processing (EURASIP)


Sergio Barbarossa, U. of Rome “La Sapienza”

Title: Distributed sensing algorithms for cognitive networks

Abstract: Wireless sensor networks (WSN) have been proven to be very helpful in several applications. In this talk, we illustrate how cognitive radio networks can benefit from the distributed sensing performed by WSN's. We start from a decentralized sensing architecture where the sensing nodes report their measurements to a fusion center and then we move to totally distributed approaches requiring no fusion center. We show how to build interference maps with a distributed mechanism, emphasizing the role of network topology on system performance. Finally, we illustrate a joint optimization of radio resource allocation and collaborative sensing parameters, e.g. detection thresholds, sensing time and convergence time, to be used in cognitive networks.

Bio: Sergio Barbarossa (S’84–M’88–F’12) received the M.Sc. degree in 1984 and the Ph.D. degree in electrical engineering in 1988, both from the University of Rome “La Sapienza,” Rome, Italy. He has held positions as a Research Engineer with Selenia SpA (1984–1986) and with the Environmental Institute of Michigan (1988), as a Visiting Professor with the University of Virginia (1995 and 1997) and with the University of Minnesota (1999). He has taught short graduate courses at the Polytechnic University of Catalunya (2001 and 2009). Currently, he is a Full Professor with the University of Rome “La Sapienza.” His current research interests lie in the area of signal processing for self-organizing networks, vehicular networks, bio-inspired signal processing, femtocell networks, graph theory, game theory, and distributed optimization algorithms. He is the author of a research monograph titled “Multiantenna Wireless Communication Systems.” He has been the scientific lead of the European project WINSOC on wireless sensor networks, the European Project FREEDOM on femtocell networks, and he is currently the scientific lead of the TROPIC project on cloud computing over small cell networks. He is also a principal investigator in the European Project SIMTISYS, on the radar monitoring of maritime traffic from satellites. Dr. Barbarossa has been nominated as an IEEE Fellow for his contributions to signal processing, sensor networks, and wireless communications. He received the 2010 EURASIP Technical Achievements Award for his contributions to synthetic aperture radar, sensor networks, and communication networks. He received the 2000 IEEE Best Paper Award from the IEEE Signal Processing Society. He is the coauthor of papers that received the Best Student Paper Award at ICASSP 2006, SPAWC 2010, EUSIPCO 2011, and CAMSAP 2011. From 1997 until 2003, he was a member of the IEEE Technical Committee for Signal Processing in Communications. He served as an Associate Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING for two terms (1998–2000 and 2004–2006). He is now a member of the IEEE SIGNAL PROCESSING MAGAZINE Editorial Board. He has been the General Chairman of the IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2003. He has been the Guest Editor for Special Issues on the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, EURASIP Journal of Applied Signal Processing, EURASIP Journal on Wireless Communications and Networking, and the IEEE SIGNAL PROCESSING MAGAZINE. In 2012, he was nominated a IEEE Distinguished Lecturer from the Signal Processing Society.


Yonina Eldar, Technion, Israel

Title: Sub-Nyquist Sampling: Bounds, Correlations and Hardware

Abstract: The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Consequently, conversion to digital has become a serious bottleneck. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power. In this talk, we present a framework for sampling and processing a wide class of wideband analog signals at rates far below Nyquist. We refer to this methodology as Xampling: A combination of compression and sampling, performed simultaneously. Using the Cramer-Rao bound we develop a generic low-rate sampling architecture that is optimal in a mean-squared error sense, and can be applied to a wide variety of wideband inputs. The resulting system can be readily implemented in hardware, and is easily modified to incorporate correlations between signals. We consider an application of these ideas to ultrasound imaging and demonstrate recovery of noisy ultrasound images from sub-Nyquist samples while performing beamforming in the compressed domain. Applications to a variety of additional problems in radar and communications will also be described. Finally, motivated by problems in optics, we extend these principles to nonlinear problems leading to quadratic and more general nonlinear compressed sensing techniques. We demonstrate applications to phase recovery from magnitude measurements and super-resolution imaging.

Bio: Yonina Eldar received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering both from Tel-Aviv University (TAU), Tel-Aviv, Israel, in 1995 and 1996, respectively, and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge, in 2002. She is currently a Professor in the Department of Electrical Engineering at the Technion—Israel Institute of Technology, Haifa. She is also a Research Affiliate with the Research Laboratory of Electronics at MIT and a Visiting Professor at Stanford University, Stanford. Dr. Eldar was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. In 2004, she was awarded the Wolf Foundation Krill Prize for Excellence in Scientific Research, in 2005 the Andre and Bella Meyer Lectureship, in 2007 the Henry Taub Prize for Excellence in Research, in 2008 the Hershel Rich Innovation Award, the Award for Women with Distinguished Contributions, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion Outstanding Lecture Award, in 2009 the Technion's Award for Excellence in Teaching, in 2010 the Michael Bruno Memorial Award from the Rothschild Foundation, and in 2011 the Weizmann Prize for Exact Sciences. In 2012 she was elected to the Young Israel Academy of Science and to the Israel Committee for Higher Education, and elected an IEEE Fellow. She received several best paper awards together with her research students and colleagues. She is a Signal Processing Society Distinguished Lecturer, and an Editor-in-Chief of Foundations and Trends in Signal Processing.


Geert Leus, TU Delft

Title: Compressive Power Spectrum Sensing

Abstract: Spectrum sensing is a crucial ingredient of various types of applications, such as frequency spectrum sensing for cognitive radio and angular spectrum sensing for direction of arrival estimation. A popular tool that has recently been introduced for spectrum sensing is compressive sampling. Adopting this technique, it is possible to sample the measured signal below the Nyquist rate without compromising the reconstruction error, under the condition that the measured signal is sparse in some domain (frequency, angular, etc.). Current compressive spectrum sensing techniques mainly focus on reconstructing the signal itself. However, for many applications, this is overkill and estimating the power on every frequency or angle is sufficient. In this plenary, we therefore present a new framework for reconstructing the power spectrum from compressive measurements, labeled as compressive power spectrum sensing. This allows for improved compression rates, and if designed properly, it even works without any sparsity constraints on the spectrum, i.e., it can also be used to reconstruct non-sparse spectra. In this talk, we will first introduce the concept of compressive power spectrum sensing, and then more specifically focus on reconstructing frequency and angular power spectra from compressive measurements using for instance multi-coset sampling and non-uniform antenna arrays.

Bio: Geert Leus received the electrical engineering degree and the PhD degree in applied sciences from the Katholieke Universiteit Leuven, Belgium, in June 1996 and May 2000, respectively. Currently, Geert Leus is an „Antoni van Leeuwenhoek“ Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the area of signal processing for communications. Geert Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a Fellow of the IEEE. Geert Leus was the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, and an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, and the IEEE Signal Processing Letters. Currently, he is a member of the IEEE Sensor Array and Multichannel Technical Committee and serves as the Editor in Chief of the EURASIP Journal on Advances in Signal Processing.


Ken Ma, CUHK, Hong Kong

Title: Semidefinite Relaxation: Classical Developments and Forefront Advances

Abstract: Semidefinite relaxation (SDR) has recently been recognized as a very useful and handy tool in signal processing and communications. It is a powerful approximation technique for a host of difficult optimization problems, usually taking the form of nonconvex quadratically constrained quadratic programs. SDR has found numerous applications; among them particularly important applications are MIMO detection, sensor network localization, and transmit beamforming (which covers classical single-cell multiuser downlinks, multicell coordinated multiuser downlinks, unicasting and multicasting, cognitive radio, physical layer security, one-way and two-way relays…). This talk will give an overview on SDR, covering the basic ideas, how it operates in practice, its applications, and key theoretical results. Some forefront advances of SDR in specific applications will also be described.

Bio: Wing-Kin Ma received the B.Eng. degree in electrical and electronic engineering from the University of Portsmouth, Portsmouth, U.K., in 1995 and the M.Phil. and Ph.D. degrees, both in electronic engineering, from the Chinese University of Hong Kong (CUHK), Hong Kong, in 1997 and 2001, respectively. He is currently an Assistant Professor with the Department of Electronic Engineering, CUHK. From 2005 to 2007, he was also an Assistant Professor with the Institute of Communications Engineering, National Tsing Hua University, Taiwan, R.O.C. Prior to that, he held various research positions with McMaster University, Canada; CUHK; and the University of Melbourne, Australia. His research interests are in signal processing and communications, with a recent emphasis on MIMO communication, convex optimization, blind source separation, and signal processing for hyperspectral remote sensing. Dr. Ma is currently Guest Editor of IEEE Journal of Selected Areas in Communications on the special issue “Signal Processing Techniques for Wireless Physical Layer Security,” and IEEE Signal Processing Magazine on the special issue “Signal and Image Processing in Hyperspectral Remote Sensing.” He previously served as Associate Editor of IEEE Transactions on Signal Processing and IEEE Signal Processing Letters, and Guest Editor of IEEE Signal Processing Magazine. He was a Speaker of a Tutorial in EUSIPCO 2011. He is a recipient of the 2009 Exemplary Teaching Award given by the Faculty of Engineering, CUHK, and co-recipient of an ICASSP 2011 Best Student Paper Award and a WHISPERS 2011 Best Paper Award.


Bjorn Ottersten, U. of Luxembourg, Luxembourg; and Royal Inst. of Tech., Stockholm, Sweden

Title: Multi-antenna Signal Processing in Satellite Communications

Abstract.: Commercial satellite communication systems are facing increased competition from terrestrial communication networks for content delivery. Innovative and cost efficient applications and services provided by satellite systems must evolve. Satellite broadcast services provide an unprecedented coverage at low cost. A wide range of satellite communication applications can be envisioned included multimedia delivery, traffic information, fleet management, software downloads, and public safety communications etc. The commercial success of such services requires reliable and secure delivery to a wide range of users. We discuss some challenges in designing novel systems which combine information from multiple cooperating satellites and/or terrestrial transmitters attempting to minimize latency and transceiver complexity. Advanced transmission and reception schemes based on interference rejection and multi-user detection allow increased spectral efficiency, higher throughput, more reliable communication, small dish antennas etc. Cooperative transmission and reception techniques based on Multiple-Input Multiple-Output (MIMO) communications allow reliable provisioning of two-way broadband, interactive and mobile applications in satellite systems.

Bio: Björn Ottersten was born in Stockholm, Sweden, 1961. He received the M.S. degree in electrical engineering and applied physics from Linköping University, Linköping, Sweden, in 1986. In 1989 he received the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA. Dr. Ottersten has held research positions at the Department of Electrical Engineering, Linköping University, the Information Systems Laboratory, Stanford University, the Katholieke Universiteit Leuven, Leuven, and the University of Luxembourg. During 96/97 Dr. Ottersten was Director of Research at ArrayComm Inc, a start-up in San Jose, California based on Ottersten’s patented technology. He has co-authored journal papers that received the IEEE Signal Processing Society Best Paper Award in 1993, 2001, and 2006 and 3 IEEE conference papers receiving Best Paper Awards. In 1991 he was appointed Professor of Signal Processing at the Royal Institute of Technology (KTH), Stockholm. From 1992 to 2004 he was head of the department for Signals, Sensors, and Systems at KTH and from 2004 to 2008 he was dean of the School of Electrical Engineering at KTH. Currently, Dr. Ottersten is Director for the Interdisciplinary Centre for Security, Reliability and Trust at the University of Luxembourg. As Digital Champion of Luxembourg, he acts as an adviser to European Commissioner Neelie Kroes. Dr. Ottersten has served as Associate Editor for the IEEE Transactions on Signal Processing and on the editorial board of IEEE Signal Processing Magazine. He is currently editor in chief of EURASIP Signal Processing Journal and a member of the editorial boards of EURASIP Journal of Applied Signal Processing and Foundations and Trends in Signal Processing. Dr. Ottersten is a Fellow of the IEEE and EURASIP. In 2011 he received the IEEE Signal Processing Society Technical Achievement Award. He is a first recipient of the European Research Council advanced research grant. His research interests include security and trust, reliable wireless communications, and statistical signal processing.


P.P. Vaidyanathan, Caltech

Title: Sparse sensing with coprime sampling lattices

Abstract: Imagine we have a pair of uniform samplers operating simultaneously on a signal. With the sampling rates arbitrarily small, is it possible to extract any useful information about the signal at all? It turns out that under some conditions of stationarity it is possible to recover second order statistical information, which is sufficient for many applications. For example, from samples of x(t) taken at the sparse rates fs=M and fs=N (where M and N are arbitrarily large but coprime), it is possible to estimate the autocorrelations at the dense sampling rate fs: The enabling principle in these applications comes from the theory of sparse coprime sampling. This talk focusses on this theory. Although the technique has surfaced in some applications in the distant past, a systematic development of the theory has evaded attention. We discuss both the one and multidimensional cases, and present applications both for temporal and spatial signals. In the multidimensional case, coprime sampling involves the construction of coprime lattices, a very interesting problem mathematically. One outcome of the theory is that under some conditions it is possible to combine two DFT filter banks with M and N bands and make them operate like an MN-band filter bank. Similarly it is possible to combine two sensor arrays with M and N sensors, such that there are O(MN) degrees of freedom, say, for beamforming and direction-of-arrival estimation (e.g., O(MN) sources can be identified with M +N sensors). Similarly it is possible to compute a spectrum with resolution proportional to 1=MN by combining two systems which would individually produce only resolutions of 1=N and 1=M: A nonstandard application of this theory is in channel identification: somewhat surprisingly, a complex channel (not just second-order information) can be identified by sending a pulse stream at an arbitrarily low rate and sampling the channel output at another arbitrarily low rate, coprimaly related to the transmission rate. This talk will give an overview of sparse coprime sampling theory, and elaborate on some of the above applications.

Bio: P. P. Vaidyanathan has been with the California Institute of Technology since 1983. His main research interests are in digital signal processing, multirate systems, wavelet transforms, digital communications, genomic signal processing, radar signal processing, and sparse signal processing. He has authored more than 400 papers in journals and conferences, and is the author of four books: Multirate systems and filter banks (Prentice Hall, 1993), Linear Prediction Theory (Morgan and Claypool, 2008), Signal Processing and Optimization for Transceiver Systems (Cambridge University Press, 2010), and Filter bank transceivers for OFDM and DMT systems (Cambridge University Press, 2011). He was recipient of the award for excellence in teaching at the California Institute of Technology multiple times. His papers have received awards from IEEE and from the IETE (Institute of Electronics and Telecommunications Engineers, India). Dr. Vaidyanathan is a Fellow of the IEEE, recipient of the F. E. Terman Award of the American Society for Engineering Education, past distinguished lecturer for the IEEE Signal Processing Society, recipient of the IEEE CAS Society Golden Jubilee Medal, the IEEE Signal Processing Society Technical Achievement Award (2002), and the IEEE Signal Processing Society Education Award (2012).


Alle-Jan van der Veen, TU Delft

Title: Signal Processing Tools for Radio Astronomy

Abstract: Radio astronomy is known for its very large telescope dishes, but is currently making a transition towards the use of large numbers of small elements. For example, the Low Frequency Array, commissioned in 2010, uses about 50 stations, each consisting of at least 96 low band antennas and 768 high band antennas. For the Square Kilometer Array, planned for 2024, the numbers will be even larger.

These instruments pose interesting array signal processing challenges. To present some aspects, we start by describing how the measured correlation data is traditionally converted into an image, and translate this into an array signal processing framework. This paves the way for a number of alternative image reconstruction techniques, such as a Weighted Least Squares approach. Self-calibration of the instrument is required to handle instrumental effects such as the unknown, possibly direction dependent, response of the receiving elements, as well as a unknown propagation conditions through the Earth's troposphere and ionosphere. Array signal processing techniques seem well suited to handle these challenges. The fact that the noise power at each antenna element may be different motivates the use of Factor Analysis, as a more appropriate alternative to the eigenvalue decomposition that is commonly used in array processing. Factor Analysis also proves to be very useful for interference mitigation. Interestingly, image reconstruction, calibration and interference mitigation are often intertwined in radio astronomy, turning this into an area with very challenging signal processing problems.

Bio: Alle-Jan van der Veen (IEEE Fellow, 2005) was born in The Netherlands in 1966. He received the Ph.D. degree (cum laude) from TU Delft, The Netherlands, in 1993. Throughout 1994, he was a postdoctoral scholar at Stanford University, Stanford, CA. At present, he is a Full Professor in signal processing at TU Delft. His research interests are in the general area of system theory applied to signal processing, and in particular algebraic methods for array signal processing, with applications to wireless communications and radio astronomy. Dr. van der Veen is the recipient of the 1994 and the 1997 IEEE Signal Processing Society (SPS) Young Author Paper Award. He was Chairman of the IEEE SPS Signal Processing for Communications Technical Committee from 2002 to 2004, the Editor-in-Chief of the IEEE Signal Processing Letters from 2002 to 2005, and the Editor-in-Chief of the IEEE Transactions on Signal Processing from 2006 to 2008. He was Technical Cochair of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011, and currently chairs the IEEE SPS Fellow Reference Committee.


Wotao Yin, Rice U.

Title: Distributed and decentralized sparse optimization

Abstract: Sparse optimization has found interesting applications in many data-processing areas such as machine learning, signal processing, compressive sensing, medical imaging, finance, etc. This talk reviews existing sparse optimization algorithms and introduces ones tailored for very large scale sparse optimization problems with terabytes of data through distributed and/or decentralized computation. The talk introduces ideas that enable the state-of-the-art sequential algorithms such as the fast iterative soft-thresholding algorithm (FISTA), alternating direction method of multipliers (ADMM), linear Bregman method (LBreg), and greedy block coordinate-descent method (GBCD) for parallel and even decentralized processing of data. Besides the typical complexity analysis, we analyze the increased iterations and communication overhead due to parallelization or decentralization. Numerical results are presented to demonstrate the scalability of the parallel codes for handling tera-scale problems.

Bio: Wotao Yin is an associate professor with Rice University, the Department of Computational and Applied Mathematics. His research interests lie in computational optimization and its applications in image processing, machine learning, medical imaging, and other inverse problems. He received his B.S. in mathematics from Nanjing University in 2001, and then M.S. and Ph.D. in operations research from Columbia University in 2003 and 2006, respectively. Since 2006, he has been with Rice University. He won NSF CAREER award in 2008 and Alfred P. Sloan Research Fellowship in 2009. His recent work has been in optimization algorithms for large-scale and distributed signal processing and machine learning problems.


+ CHAIR'S Bonus:

Georgios Giannakis, U. Minnesota

Title: Compendious State Inference for Wireless Cognitive Networks

Abstract.: Sensing is a critical prerequisite in envisioned applications of wireless cognitive radio (CR) networks, which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. This talk presents recent advances for comprehensive situation awareness at the PHY and higher-layers of CR networks. PHY-layer sensing capitalizes on the novel notion of spatio-temporal RF cartography, which amounts to constructing two families of maps: (m1) global power spectral density maps capturing the distribution of power across space, time, and frequency; and (m2) channel gain maps providing the propagation medium per frequency from each node to any point in space and time. Higher-layer sensing aims to comprehensively yet concisely capture key state variables which in addition to interference and any-to-any link gains, include band occupancies, queue lengths, path delays, and possible anomalies. The vision is to construct a compendious cognition infrastructure so as to maximize overall network performance and end-user satisfaction, notwithstanding the significant challenges associated with the CR spectrum access paradigm, as well as the incomplete, corrupt and sporadic data, which reflect the cost and restrictions in acquiring network state measurements. This is vision is realized by leveraging advances in machine learning and statistical signal processing, which include kernel-based inference, (dynamic) nonparametric basis pursuit, matrix completion, and dictionary learning.

Bio: G. B. Giannakis (IEEE Fellow'97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. Since 1999 he has been a professor with the Univ. of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published two edited books, two research monographs, 20 book chapters, 350 journal and 580 conference papers (H-index=103). Current research focuses on compressive sampling, cognitive radios, cross-layer designs, wireless sensors, social and power grid networks. He is the (co-) inventor of 21 patents issued, and the (co-) recipient of 8 best paper awards

from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, and the G. W. Taylor Award for Distinguished Research from the University of Minnesota. He is a Fellow of EURASIP, and has served the IEEE in a number of posts, including that of a Distinguished Lecturer for the IEEE-SP Society.


Nikos Sidiropoulos, U. Minnesota

Title: Analyzing (Big) Data Boxes: From Uniqueness of Tensor Decomposition to Multi-Way Compressed Sensing

Abstract: Low-rank decomposition (or approximation) is a key tool for the analysis of tensor data. An important reason for this is that the latent factors are essentially unique in the case of low-rank tensor decomposition, unlike matrix decomposition. We will begin with a retrospective on uniqueness issues, from the early results to more recent ones, which have pushed the boundary of when uniqueness holds almost surely. We will also touch upon the main algorithmic approaches for low-rank tensor approximation, from Alternating Least Squares to very recent work dealing with scalable computation on Hadoop/MapReduce. When the tensor is too big to fit in main memory, one possibility is to spawn parallel processing threads that analyze judiciously sampled parts of the tensor. An alternative is to compress the big tensor down to a far smaller one that fits in main memory, in a way that preserves the latent low-rank structure. Towards this end, a multi-linear extension of compressed sensing to multi-way tensor compression will be presented, which allows exact recovery of the latent factors of the big tensor from the compressed data.

Bio: Nicholas Sidiropoulos (Fellow, IEEE) received the Diploma in Electrical Engineering from the Aristotelian University of Thessaloniki, Greece, and M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland—College Park, in 1988, 1990 and 1992, respectively. He has served as Assistant Professor in the Department of Electrical Engineering at the University of Virginia (1997-1999); Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota—Minneapolis (2000-2002); Professor in the Department of Electronic and Computer Engineering at the Technical University of Crete, Chania—Crete, Greece (2002-2011); and Professor in the Department of Electrical and Computer Engineering at the University of Minnesota—Minneapolis (2011-). His research interests are in signal processing for communications, convex optimization, cross-layer resource allocation for wireless networks, and multiway analysis – i.e., linear algebra for data arrays indexed by three or more variables. His current research focuses primarily on signal and tensor analytics, with applications in cognitive radio, big data, and preference measurement. He received the NSF/CAREER award in 1998, and the IEEE Signal Processing Society (SPS) Best Paper Award in 2001, 2007, and 2011. He served as IEEE SPS Distinguished Lecturer (2008-2009), and as Chair of the IEEE Signal Processing for Communications and Networking Technical Committee (2007-2008). He received the 2010 IEEE Signal Processing Society Meritorious Service Award.