Carlo Fischione

KTH Royal Institute of Technology, Sweden

Carlo Fischione

KTH Royal Institute of Technology, Sweden


Dr. Carlo Fischione is full Professor of Internet of Things at KTH Royal Institute of Technology, Electrical Engineering and Computer Science, Division of Network and Systems Engineering, Stockholm, Sweden. He is Director of the KTH Data Science Micro Degree Program, an advanced study program for Ericsson researchers worldwide. He received the Ph.D. degree in Electrical and Information Engineering (3/3 years) in May 2005 and the Laurea degree in Electronic Engineering (Laurea, Summa cum Laude, 5/5 years) in April 2001 from from University of L’Aquila, Italy, He has held research positions at Massachusetts Institute of Technology, Cambridge, MA (2015, Visiting Professor); Harvard University, Cambridge, MA (2015, Associate); and University of California at Berkeley, CA (2004-2005, Visiting Scholar, and 2007-2008, Research Associate).

His research interests include applied optimization, wireless, sensor networks, Internet of things, and machine learning. He has co-authored over 200 publications, including a book, book chapters, international journals and conferences, and international patents. He received a number of awards, such as the “IEEE Communication Society S. O. Rice” award for the best IEEE Transactions on Communications paper of 2018, the best paper award of IEEE Transactions on Industrial Informatics (2007), the best paper awards at the IEEE International Conference on Mobile Ad-hoc and Sensor System 05 and 09 (IEEE MASS 2005 and IEEE MASS 2009), the Best Paper Award of the IEEE Sweden VT-COM-IT Chapter (2014), the Best Business Idea awards from VentureCup East Sweden (2010) and from Stockholm Innovation and Growth (STING) Life Science in Sweden (2014), the “Ferdinando Filauro” award from University of L’Aquila, Italy (2003), the “Higher Education” award from Abruzzo Region Government, Italy (2004), the Junior Research award from Swedish Research Council (2007), and the “Silver Ear of Wheat” award in history from the Municipality of Tornimparte, Italy (2012).

He is Editor of IEEE Transactions on Communications and IEEE Journal on Selected Areas on Communications – Series on Machine Learning for Communication and Networking, and has been serving as Associated Editor of IFAC Automatica (2014-2019). Meanwhile, he also has offered his advice as a consultant to numerous technology companies such as ABB Corporate Research, Berkeley Wireless Sensor Network Lab, Ericsson Research, Synopsys, and United Technology Research Center. He is co-founder, vice-president and scientific director of ELK.

He is Member of IEEE (the Institute of Electrical and Electronic Engineers), and Ordinary Member of DASP (the Italian academy of history Deputazione Abruzzese di Storia Patria).

Tutorial: “Machine Learning for the Internet of Things

Abstract: One of the main characteristics of the Internet of Things (IoT) technological revolution is the generation of huge quantities of data. Such wealth of data and their use in several new IoT technologies is forcefully motivating the development of data analysis  methods, namely machine learning. Currently, machine learning needs big datasets and very huge computational and communication resources. However, in IoT, data sets of any size will be distributed among several nodes (people, devices, objects, or machines) that might not be able to perform the computations and to share data.

Unfortunately, existing machine learning methods are mostly intended for proprietary or high performing networks such as in data centres. They would greatly stress the public communication networks of IoT, such as 5G wireless networks and beyond. In these networks, machine learning methods will encounter new challenges in terms of computation, bandwidth, scalability, privacy, and security.  Machine learning over networks face a lack of understanding of the fundamental methods, and poor performance of their algorithms.
In this tutorial, we highlight the need of establishing a new fundamental theory for machine learning over networks and in particular IoT networks.

Keynote: “Co-design of Machine Learning and Wireless Communications”

Abstract: In this keynote, we will build on the introduction provided by the tutorial lecture and we give an overview of the state-of-the-art about Machine Learning over IoT. We will present some of our recent research developments in this field, with particular reference to communication efficient distributed machine learning, wireless for machine learning, and joint optimisation of wireless communication and machine learning services.

All sessions by Carlo Fischione

Phd School – December 3 – Afternoon

03 Dec 2020
14:30 - 18:00

This site uses cookies. Click Accept to agree. To know more: Cookie Policy

This site uses cookies to provide the best browsing experience possible. By continuing to use this site without changing your cookie settings or by clicking on "Accept", you allow their use.