Ascolta
Si informano gli studenti interessati che giorno 21 febbraio 2020 alle ore 15.00 presso l'Aula Savagnone il Prof. Donato Rossi dell'Università degli Studi di Padova terrà un seminario dal titolo: "Machine-learning for 5G Mobile Networks: a pragmatic essay on where, how and why."
Verrà attribuito 1 CFU agli studenti del Corso di Laurea in Ingegneria Elettronica e del Corso di Laurea Magistrale in Electronics Engineering che seguiranno il seminario e supereranno la prova finale prevista.
Speaker: Michele Rossi
Short Bio: Michele Rossi is an Associate Professor with the Department of Information Engineering at the University of Padova, Italy. His research interests lie in wireless sensing, green mobile networks, edge and wearable computing. In the last few years, he has been actively involved in EU projects on IoT technology (IOT-A, FP7-ICT- 2009-5, project no. 257521) and has collaborated with SMEs such as Worldsensing (Barcelona, ES) in the design of optimized IoT solutions for smart cities and with large companies such as SAMSUNG and INTEL, developing technologies within the wireless health and IoT domains. In 2014, he has been the recipient of a SAMSUNG GRO award with a project entitled ``Boosting Efficiency in Biometric Signal Processing for Smart Wearable Devices''. In 2016-2018, he has been collaborating with INTEL on the design of IoT protocols exploiting cognition and machine learning, as part of the INTEL Strategic Research Alliance (ISRA) R&D program. His research is currently supported by the European Commission through the H2020 ITN SCAVENGE project (project no. 675891) on "green 5G networks" and by the H2020 ITN MINTS (project no. 861222) on "millimeter-wave networking and sensing for beyond 5G networks". Dr. Rossi has been the recipient of six best paper awards from the IEEE, currently serves on the Editorial Boards of the IEEE Transactions on Mobile Computing and of the IEEE Open Journal of the Communications Society (OJ-COMS). He is a Senior Member of the IEEE. Web:
http://www.dei.unipd.it/~rossi/
Abstract: in this talk, I discuss some selected applications of Machine Learning (ML) algorithms to modern mobile systems. I follow a pragmatic approach, leveraging my recent research work, which encompasses algorithms for energy efficiency, Quality of Service (QoS) enhancements and the use of inference tools for the analysis and prediction of context information (traffic, mobility, etc.). My objective is to show where (which applications), how (in combination with which techniques) and why (the benefits) ML may be useful, with an emphasis on the how, i.e., what may be good usage models for ML (e.g., open- vs closed-loop) in conjunction with optimization and control algorithms for wireless networks, and on the adopted learning strategies (e.g., supervised vs unsupervised).
