ABOUT
Osvaldo Simeone is a Professor of Information Engineering with the Centre for Telecommunications Research at the Department of Informatics of King’s College London. He received an M.Sc. degree (with honors) and a Ph.D. degree in information engineering from Politecnico di Milano, Milan, Italy, in 2001 and 2005, respectively. From 2006 to 2017, he was a faculty member of the Electrical and Computer Engineering (ECE) Department at New Jersey Institute of Technology (NJIT), where he was affiliated with the Center for Wireless Information Processing (CWiP). His research interests include wireless communications, information theory, optimization and machine learning. Dr Simeone is a co-recipient of the 2018 IEEE Signal Processing Best Paper Award, the 2017 JCN Best Paper Award, the 2015 IEEE Communication Society Best Tutorial Paper Award and of the Best Paper Awards of IEEE SPAWC 2007 and IEEE WRECOM 2007. He was awarded a Consolidator grant by the European Research Council (ERC) in 2016. His research has been supported by the U.S. NSF, the ERC, the Vienna Science and Technology Fund, as well as by a number of industrial collaborations. He currently serves in the editorial board of the IEEE Signal Processing Magazine, and he is a Distinguished Lecturer of the IEEE Information Theory Society. Dr Simeone is a co-author of two monographs, an edited book published by Cambridge University Press, and more than one hundred research journal papers. He is a Fellow of the IET and of the IEEE.
Tutorial: When Can Machine Learning Be Useful for Communication Systems?
Abstract: Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems in which the deployment of conventional engineering solutions is challenged by modeling or algorithmic deficiencies. The talk starts by addressing the questions of why and when such techniques can be useful. Short introductions are then provided for supervised, unsupervised, and reinforcement learning problems. Finally, some exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud of the network at different layers of the protocol stack.
Keynote: Spiking Neural Networks: A Stochastic Signal Processing Perspective
Abstract: Spiking Neural Networks (SNNs) are distributed systems whose computing elements, or neurons, are characterized by analog internal dynamics and by digital and sparse inter-neuron, or synaptic, communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by hardware implementations to obtain significant energy reductions as compared to conventional Artificial Neural Networks (ANNs). SNNs can be used not only as co-processors to carry out given computing tasks, such as classification, but also as learning machines that adapt their internal parameters, e.g., their synaptic weights, on the basis of data and of a learning criterion. This talk provides an overview of models, learning rules, and applications of SNNs from the viewpoint of stochastic signal processing.