Graph Signal Processing: Fundamentals and Applications to Diffusion Processes

The tutorial consists of two parts of similar length: an introduction to the basics of Graph Signal Processing (GSP), which will review and illustrate main existing results, and the application of GSP-tools to distributed network processing and diffusion processes over networks. The first part introduces the field of GSP, motivates its usefulness via meaningful applications, and presents in a didactic yet concise manner its foundational concepts, which have been derived over the past five years. The second part focuses on contemporary results. We will first illustrate that GSP is well suited to model and study diffusion processes over networks. With this premise in mind, we revisit classical SP problems such as sampling, interpolation, system identification, and filtering. We first present the theoretical results and then discuss their implications for distributed and dynamic processing. Furthermore, we illustrate the utility of applying GSP to analyze dynamics on networks through a diverse gamut of applications from social sciences to biology, spanning well-established problems like consensus and emerging neuroscience challenges like brain state induction.

The authors and co-presenters of this tutorial are:

This tutorial will be presented at SAM'16, EUSIPCO'16, and GlobalSIP'16.

If you are interested in the tutorial slides, please send me an email at gmateosb at ece dot rochester dot edu

Signal Processing for Big Data

We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. The sheer volume of data makes it often impossible to run analytics using a central processor and storage, and distributed processing with parallelized multi-processors is preferred while the data themselves are stored in the cloud. As many sources continuously generate data in real time, analytics must often be performed "on-the-fly" and without an opportunity to revisit past entries. Due to their disparate origins, the resultant datasets are often incomplete and include a sizable portion of missing entries. In addition, massive datasets are noisy, prone to outliers, and vulnerable to cyber-attacks. These effects are amplified if the acquisition and transportation cost per datum is driven to a minimum. Overall, Big Data present challenges in which resources such as time, space, and energy, are intertwined in complex ways with data resources.

Given these challenges, ample signal processing (SP) opportunities arise. This tutorial seeks to provide an overview of ongoing research in novel models applicable to a wide range of Big Data analytics problems, as well as algorithms and architectures to handle the practical challenges, while revealing fundamental limits and insights on the mathematical trade-offs involved. The goal is to selectively cover a diverse gamut of Big Data challenges and opportunities through a comprehensive tutorial surveying methodological advances, as well as more application-oriented topics and illustrative examples.

The authors and co-presenters of this tutorial are:

This tutorial will be presented at ICASSP'14 (Photo 1, Photo 2) and EUSIPCO'14 (Photo 1, Photo 2). An updated version of this tutorial entitled "Signal Processing Tools for Big Data Analytics" will be presented at ICASSP'15, EUSIPCO'15, and SPAWC'16. A survey paper which served as the basis for this tutorial is:

  • K. Slavakis, G. B. Giannakis, and G. Mateos, ``Modeling and optimization for Big Data analytics,'' IEEE Signal Processing Magazine - Special issue on `Signal processing for Big Data,' vol. 31, no. 5, pp. 18-31, September 2014.

If you are interested in the tutorial slides, please send me an email at gmateosb at ece dot rochester dot edu

Cartography for Cognitive Networks

Communication networks have evolved from specialized, research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer-oriented, and heterogeneous. Propelled by emergent social networking services and high-definition streaming platforms, network traffic has grown explosively thanks to the advances in processing speed and storage capacity of state-of-the-art communication technologies.

As "netizens" demand a seamless networking experience that entails not only higher speeds, but also resilience and robustness to failures and malicious cyber-attacks, ample opportunities for signal processing (SP) research arise. The vision is for ubiquitous smart and cognitive network devices to enable data-driven statistical learning algorithms for distributed, robust, and online network operation and management, adaptable to the dynamically-evolving network landscape with minimal need for human intervention. The present tutorial aims at delineating the analytical background and relevance of SP tools to dynamic network monitoring, introducing the notion of network cartography -- a framework to construct maps of the dynamic network state in an efficient and scalable manner tailored for large-scale cognitive networks.

The authors and co-presenters of this tutorial are:

This tutorial will be presented at SAM'14, ICC'13 and Globecom'13. A survey paper which served as the basis for this tutorial is:

  • G. Mateos and K. Rajawat, ``Dynamic network cartography,'' IEEE Signal Processing Magazine - Special issue on `Adaptation and learning over complex networks,' vol. 30, no. 3, pp. 129-143, May 2013. Errata

If you are interested in the tutorial slides, please send me an email at gmateosb at ece dot rochester dot edu