Plenary Speakers

Wednesday 21 May 2014

Plenary session, W1-A, 9.00-9.50
Session chair: Moncef Gabbouj (Tampere University of Technology)
Signal Processing for Big Data
Jose Moura (Carnegie Mellon University, USA)


In the era of Big Data are traditional analysis tools and concepts drawn from signal processing any useful? In social contexts, or the web, or enterprises, the relations and dependencies among data are often conveniently represented by graphs, with the data becoming functions or signals on a graph-a point of view structurally different from the one traditionally adopted with time series. This talk discusses extensions of the basic concepts of signal processing to graph signals: filters and filtering, shifting, frequency, low-, high-pass graph signals, frequency response, linear transforms, Fourier and z-transforms. We then illustrate signal processing on graphs with datasets from various contexts including social networks, a service provider, and the web.

Work with Dr. Aliaksei Sandryhaila and graduate student Jonathan Mei.

Prof. Mura

Professor José M. F. Moura

José M. F. Moura is the Philip and Marsha Dowd University Professor at Carnegie Mellon University, with the Electrical and Computer Engineering and, by courtesy, the BioMedical Engineering. He is a member of the US National Academy of Engineering, a corresponding member of the Portugal Academy of Science, an IEEE Fellow, and a Fellow of the AAAS. He holds a D. Sc. in Electrical Engineering and Computer Science, M.Sc., and EE degrees all from MIT and an EE degree from Instituto Superior Técnico (IST, Portugal). Moura's research interests are in statistical signal and image processing. He is working in the new area of Big Data and network science, with particular emphasis on distributed decision and inference in networked systems and graph based data. Moura received the the 2010 Technical Achievement and 2012 Society Award of the IEEE Signal Processing Society. He is on the Board of Directors of the IEEE and serves as IEEE Division IX Director (201-2013). He was the President of the IEEE Signal Processing Society (2008- 2009). He was Editor in Chief of the IEEE Transactions on Signal Processing and acting Editor in Chief for the IEEE Signal Processing Letters. He was on the Editorial Board of several Journals

Plenary session, W2-A, 14.30-15.20
Session chair: Ahmed Tewfik (The University of Texas at Austin)
Machine Learning Methods in Computational Cancer Biology
Mathukumalli Vidyasagar (University of Texas at Dallas, USA)


Cancer is the second leading cause of death worldwide. Due to the diversity of manifestations of cancer, personalized therapy is imperative. In this talk, three distinct research themes are discussed, that together go a long way towards achieving this objective, namely: sparse classification, sparse regression, and network inference. The mathematical formulations of each theme are presented, current status is summarized, and problems for future research are indicated. Actual applications to various forms of cancer are also included.

Prof. Vidyasagar

Professor Mathukumalli Vidyasagar

Dr. Vidyasagar received all of his degrees in Electrical Engineering at the University of Wisconsin, Madison, including the Ph.D. in 1969. He spent the next twenty years primarily at Concordia University, Montreal and the University of Waterloo, Waterloo, both in Canada. In 1989 he returned to India as the Director of the Centre for Artificial Intelligence and Robotics in Bangalore. In 2000 he moved to the private sector as an Executive Vice President of Tata Consultancy Services, India's largest IT company and one of the ten largest in the world. In 2009 he retired from TCS and joined the University of Texas at Dallas as Cecil & Ida Green Chair in Systems Biology Science, the position that he holds today. Vidyasagar is the author of nearly 140 journal articles and eleven books, spanning several areas such as linear systems, nonlinear systems, robotics, statistical learning theory, stochastic processes, and computational biology. During his career Vidyasagar has received several honors, including the IEEE Control Systems (Field) Award, the ASME Rufus Oldenburger Medal, and Fellowship of The Royal Society, the oldest scientific society in the world in continuous existence. His current research interests are in stochastic processes and the computational biology of cancer.

Thursday 22 May 2014

Plenary session, T1-A, 09.00-09.50
Session chair: Thanos Stouraitis (University of Patras)
Mobile Visual Search - Linking Virtual and Physical Worlds
Bernd Girod (Stanford University, USA)


Visual search and augmented reality afford a host of intriguing new research problems at the intersection of computer vision, compression, and system architecture. For object recognition on mobile devices, a visual data base is typically stored in the cloud. Hence, for a visual comparison, information must be either uploaded from, or downloaded to, the mobile over a wireless link. The response time of the system critically depends on how much information must be transferred in both directions, and efficient compression is the key to a good user experience. We review recent advances in mobile visual search, using compact feature descriptors, and show that dramatic speed-ups and power savings are possible by considering recognition, compression, and retrieval jointly. For augmented reality applications, where image matching is performed continually at video frame rates, interframe coding of SIFT descriptors achieves bit-rate reductions of 1-2 orders of magnitude relative to advanced video coding techniques. We will use real-time implementations for different example applications, such as recognition of landmarks, media covers or printed documents, to show the benefits of implementing computer vision algorithms on the mobile device, in the cloud, or both.

Prof. Girod

Professor Bernd Girod

Bernd Girod is Professor of Electrical Engineering and (by courtesy) Computer Science in the Information Systems Laboratory of Stanford University, California, since 1999. Previously, he was a Professor in the Electrical Engineering Department of the University of Erlangen-Nuremberg. His current research interests are in the area of networked media systems. He has published over 500 conference and journal papers and 6 books, receiving the EURASIP Signal Processing Best Paper Award in 2002, the IEEE Multimedia Communication Best Paper Award in 2007, the EURASIP Image Communication Best Paper Award in 2008, the EURASIP Signal Processing Most Cited Paper Award in 2008, as well as the EURASIP Technical Achievement Award in 2004 and the Technical Achievement Award of the IEEE Signal Processing Society in 2011. As an entrepreneur, Professor Girod has been involved in several startup ventures, among them Polycom, Vivo Software, 8x8, and RealNetworks. He received an Engineering Doctorate from University of Hannover, Germany, and an M.S. Degree from Georgia Institute of Technology. Prof. Girod is a Fellow of the IEEE, a EURASIP Fellow, and a member of the German National Academy of Sciences (Leopoldina). He currently serves Stanford’s School of Engineering as Senior Associate Dean for Online Learning and Professional Development.

Plenary session, T2-A, 14.30-15.20
Session chair: Vincent Poor (Princeton University)
Information Rates for Phase Noise Channels
Gerhard Kramer (Technische Universität München, Germany)


A waveform channel is considered where the transmitted signal is corrupted by phase noise and additive white Gaussian noise. Discrete-time models are introduced that are based on multi-sample receivers. Bounds on the information rates achieved by the receivers are computed by means of numerical simulations. The results show that oversampling is beneficial for both strong and weak phase noise at high signal-to-noise ratios.

This is joint work with Hassan Ghozlan of USC. A recent paper on the topic is available at:

Prof. Kramer

Professor Gerhard Kramer

Gerhard Kramer is the Alexander von Humboldt Professor and Head of the Institute for Communications Engineering at the Technische Universität München (TUM). He received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Manitoba, Winnipeg, MB, Canada in 1991 and 1992, respectively, and the Dr. sc. techn. (Doktor der technischen Wissenschaften) degree from the ETH Zürich, Switzerland, in 1998. From 1998 to 2000, he was with Endora Tech AG, Basel, Switzerland, as a communications engineering consultant. From 2000 to 2008 he was with the Mathematics Center, Bell Laboratories, Alcatel-Lucent, Murray Hill, NJ, as a Member of Technical Staff and from 2008 to 2009 he was a Professor of Electrical Engineering at the University of Southern California (USC), Los Angeles, California. He joined TUM in 2010.

Friday 23 May 2014

Plenary session, F1-A, 09.00-09.50
Session chair: Sanjit Mitra (University of California Santa Barbara)
A Safety and Mobility (SAM) System for Urban Roads
Pravin Varaiya (University of California at Berkeley, USA)


Traffic congestion is caused by inefficient road management and by excess demand. Inefficient management is pervasive. Most urban streets and freeways do not have traffic sensing infrastructure, so one doesn’t know how much congestion there is, its cause, or whether ameliorative projects are effective. Inefficient road management must be replaced by effective feedback control of signals at intersections and at on-ramps. These control techniques are well known, but their successful implementation requires proper sensing and signal processing.
This talk describes the design and test of an experimental intersection Safety and Mobility system (SAM). SAM relies on three innovations: (1) the hardware platform that detects and records the lane, speed, signal phase and time when each vehicle enters and leaves the intersection, and the time a pedestrian is detected in the crosswalk; (2) algorithms that fuse these sensor events to estimate the intersection traffic state in real time, for use by traffic- responsive and adaptive controllers; and (3) algorithms that process and archive the time series of traffic states to produce intersection performance reports for mobility and safety.

Prof. Mura

Professor Pravin Varaiya

Pravin Varaiya is Nortel Networks Distinguished Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. From 1975 to 1992 he was also Professor of Economics at Berkeley. From 1994 to 1997 he was Director of the California PATH program, a multi-university research program dedicated to the solution of California’s transportation problems. His current research is concerned with communication networks, transportation, and hybrid systems. He has taught at MIT and the Federal University of Rio de Janeiro.
Varaiya has held a Guggenheim Fellowship and a Miller Research Professorship. He received Honorary Doctorates from L’Institut National Polytechnique de Toulouse and L’Institut National Polytechnique de Grenoble, and the Field Medal and Bode Lecture Prize of the IEEE Control Systems Society. He is a Fellow of IEEE, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.
He is on the editorial board of "Discrete Event Dynamical Systems" and "Transportation Research---C". He has co- authored three books and 300 technical papers. The second edition of "High-Performance Communication Networks" (with Jean Walrand and Andrea Goldsmith) was published by Morgan-Kaufmann in 2000. "Structure and Interpretation of Signals and Systems" (with Edward Lee) was published by Addison-Wesley in 2003. Varaiya is a member of the Board of Directors of Sensys Networks.

Plenary session, F2-A, 14.30-15.20
Session chair: Sergios Theodoridis (University of Athens)
The Aggregator Problem in the Smart Grid
Anna Scaglione (University of California, Davis, USA)


Over the past twenty years power systems and economic theory have merged to co-design of an architecture that can competitively price and dispatch in real time generators power, so as to follow the random daily electricity demand. The system is designed for reliability and to work with poor telemetry. For this reason electricity consumption is treated as being inflexible. Unfortunately, lack of control in the demand side continues to be the main bottleneck for using opportunistically abundant renewable energy, without compromising reliability. Harnessing the benefits of responsive and controllable consumption requires new technologies (hardware and software) for interacting with customers and their appliances. The talk describes the Aggregator problem, where Aggregators are the profit maximizing entities in charge for managing large populations of appliances interacting both with the customers as well as with the market. We discuss the so called dynamic pricing and direct scheduling solutions and provide medium grained models that can be used for ex-ante planning as well as for on-line scheduling of large population of appliances, with communications and computations that scale for large populations. We also indicate how these models can be the basis for offering differentiated service tariffs, while preserving the individual customer privacy.

Prof. Scaglione

Professor Anna Scaglione

Anna Scaglione is a Professor in the Electrical and Computer Engineering Department at UC Davis, and held Associate and Assistant Professor positions before at Cornell University (2001-2008) and at the University of New Mexico (2000-2001). She is a Fellow of the IEEE, and co-recipient of the 2000 IEEE Signal Processing Transactions Best Paper Award, Ellersick Best Paper Award (MILCOM 2005), the 2013 IEEE Donald G. Fink Prize Paper Award and the 2013 IEEE Signal Processing Society Young Author best paper award, with her student. She held several editorial and technical chair positions including that of Editor in Chief of the IEEE Signal Processing Letters from 2012-2013. She is currently in the Board of Governor of the Signal Processing Society. Her expertise is in the broad area of signal processing for communication systems, networks and, more recently, power systems. Her current research focuses on studying and enabling decentralized learning and signal processing in networks of sensors and on sensor systems and networking models for demand side management and sustainable energy delivery.