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News and Resources for Members of the IEEE Signal Processing Society
In this series, we aim to introduce senior society members and other experts of the signal processing field. This month, we are happy to introduce Prof. Vincenzo Matta from the Department of Information and Electrical Engineering and Applied Mathematics of the University of Salerno, Italy. His research interests are in statistical signal processing and information theory, with current emphasis on: adaptation and learning over networks, the interplay between inference, communications and security in distributed systems, multiobject/multisensor tracking and data fusion and the detection of gravitational waves.
When did you first come into touch with signal processing? What was your motivation of following a career in this domain?
I “officially” met signal processing during my master thesis. I realized that signal processing offers a unique opportunity of application to an incredible variety of research fields.
What was the first signal processing algorithm you ever implemented? In which context was it used?
A long time ago (about July 2000), my first algorithm was a Sequential Probability Ratio Test applied to the detection of gravitational waves, a (subject) matter that is quite popular in these days. Gravitational waves were observed for the first time on September 14, 2015, but not with my algorithm, alas!
What are your current research interests in the signal processing field?
Distributed inference and learning over adaptive networks; the interplay between statistics, communications and security in data networks; social data analytics; quantum information processing; sparsity-aware representations for the detection of gravitational waves.
Could you introduce an important state-of-the-art research issue (or technology) in this field?
Think about communication networks, smart grids, biological colonies, social groups, sensors, data depositories… We live pretty much embedded in a “network” world. Accordingly, network science is more and more emerging as a fundamental research field, spreading across several theoretical as well as applicative domains.
In a nutshell, the power of a network is given by some clever combination between the local cooperation among individual network units and the distributed processing of spatially dispersed pieces of information. Such a combination makes the individual agents capable of sophisticated behaviors, and of solving complex (e.g., inference, learning, optimization) tasks, much better than if they were acting individually.
Discovering the fundamental laws that govern distributed inference and learning over networks give rise to new challenges for the signal processing community, such as, among many possibilities:
A natural venue for these topics is offered by a journal that has recently joined the SPS family, the IEEE Transactions on Signal and Information Processing over Networks. In particular, there is an upcoming Special Issue of the journal specifically focused on “Inference and Learning over Networks.”
From your experience, is there something the signal processing society can learn from other societies?
I cannot say what we can learn. I can say that interaction with other communities and societies is essential to achieve successful research advances. For instance, with reference to the aforementioned network science domain, we can appreciate a remarkable and ongoing progress, which surely benefits from the fruitful collaboration among many disciplines, including signal processing, statistics, control, machine learning, computer science, optimization, physics, biology, economics, and social sciences.
From your point of view, what are the biggest challenges signal processing should solve in the next years? Do you have any comments about the development of signal processing research?
Modern times offer the challenging opportunity of mastering massive amounts of information with an increasingly large (even distributed) computational power. Such an abundance somehow “obliges” us to exploit the power of data-driven techniques, (e.g., machine learning and data mining), a land where signal processing cannot but play an active role. Leveraging the distinctive features and skills of our community, the aforementioned tools could be enriched and complemented with analytical and/or model-based solutions, in order to obtain useful physical insights, to enable a powerful understanding of the pertinent complex systems, to perform structured design and analysis, to ensure the necessary performance guarantees.
What would be your advice to a new PhD student who wants to start a career in signal processing?
My 5 golden rules:
Which application fields should be more focused in IEEE SPS publications?
Quantum information processing is becoming real. We should perhaps consider the basics of Quantum Mechanics as part of the expertise of a signal processing researcher. Sooner or later, quantum computers will be in operation, and signal processing must be ready to play a prominent role in their design and analysis.
Vincenzo Matta is an Associate Professor at the Department of Information & Electrical Engineering and Applied Mathematics of the University of Salerno, Italy. His research interests cover the wide area of statistical signal processing and information theory, with current emphasis on: adaptation and learning over networks; the interplay between inference, communications and security in distributed systems; multiobject/multisensor tracking and data fusion; detection of gravitational waves. He has published about 100 articles, on international journals, and on the proceedings of international conferences. Vincenzo Matta serves as an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks, for the IEEE Signal Processing Letters, and for the IEEE Transactions on Aerospace and Electronic Systems. He is a member of the LIGO Scientific Collaboration for the detection of gravitational waves. The teaching activity of Vincenzo Matta is in the field of statistical signal processing and communications. Specific courses offered include: Detection and Estimation Theory, Signals and Systems Theory, Information Theory, Digital Communications, Wireless Communications, and Data Networks.
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