Manuel Roveri

Politecnico di Milano

Manuel Roveri received the Dr.Eng. degree in Computer Science Engineering from the Politecnico di Milano (Italy), the MS in Computer Science from the University of Illinois at Chicago (U.S.A.) and the Ph.D. degree in Computer Engineering from Politecnico di Milano (Italy). Currently, he is an Associate Professor at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano. His research interests include intelligent embedded and cyber-physical systems, computational intelligence and adaptive algorithms. Manuel Roveri is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems. He is also Chair of the IEEE CIS Neural Networks Technical Committee (NNTC) and of the IEEE CIS Task Force on Intelligent Cyber-Physical Systems. He also served the IEEE CIS in many committees and subcommittees. He is the recipient of the 2018 IEEE Computational Intelligence Magazine “Outstanding Paper Award”, of the 2016 IEEE Computational Intelligence Society “Outstanding Transactions on Neural Networks and Learning Systems Paper Award” and the Best Paper Award at the INNS Conference on Big Data 2016.


Speeches of Manuel Roveri

Machine Learning and Internet-of-Things: opposites attract in the age of data

Internet-of-Things (IoTs) and Cyber-Physical Systems (CPSs) are gaining more and more relevance in many scientific and engineering applications and are becoming the reference technological solution for the monitoring and control of industry systems, critical infrastructures, smart grids, water distribution networks, natural or physical environments. Such systems are generally composed by a possibly large and heterogeneous set of units endowed with sensing/actuating, processing and communication abilities so as to be able to interact proactively with the environment under inspection. Interestingly, in the recent years, the pervasive dissemination of such systems and the need to satisfy their increasing demands for autonomy, energy-awareness and reliability have led embedded designers and users to move towards intelligent solutions providing units with self-adaptation, management and healing functionalities. This talk will present some fundamental intelligent technological solutions and mechanisms and show how they represent the key ingredients needed to design the current and future generation of smart IoT and CPS systems and derived applications. In particular, intelligent technological solutions and mechanisms for real-world distributed/pervasive applications will be presented and discussed.

Speech language: Italian

Topics

Machine Learning and Artificial Intelligence


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