Carlo Fischione is a Full Professor at KTH Royal Institute of Technology in the School of Electrical Engineering and Computer Science, Division of Network and Systems Engineering, Stockholm, Sweden. He is a Fellow of IEEE, KTH Digital Futures, and the Italian Academy DASP, and a Distinguished Lecturer of the IEEE Communications Society. He holds a PhD and Laurea (Summa cum Laude) in Engineering from the University of L’Aquila, Italy, and has held research positions at MIT, Harvard, and UC Berkeley. PhD in Electrical and Information Engineering, University of L’Aquila (2005) Laurea in Electronic Engineering, Summa cum Laude, University of L’Aquila (2001) His research focuses on applied optimization, wireless Internet of Things, and machine learning , with particular emphasis on federated learning, over-the-air computation, and spectrum sharing in 5G/6G networks. He leads a vibrant research group and has supervised numerous PhD and postdoctoral researchers, many of whom now hold faculty or senior research positions globally. His recent publications reflect a strong trend toward machine learning in distributed and resource-constrained networked environments , especially focusing on communication efficiency, privacy, and scalability. Key themes include federated learning over fading channels, over-the-air computation, and AI-aided wireless channel prediction. IEEE Fellow IEEE Distinguished Lecturer, Communications Society IEEE Communication Society S. O. Rice Award (2018) Best Paper Award, IEEE Transactions on Industrial Informatics (2007) Starting Grant, Swedish Research Council (2008) Prof. Fischione has advised numerous students who have gone on to prominent academic and industry roles. He has secured significant research funding from the Swedish Research Council, SSF, KAW Foundation, Vinnova, and EU Horizon programs, leading projects such as MALEN, SAICOM, TAIRCOMP, and WIDCOMP. He is also the founding General Chair of IEEE ICMLCN and a co-founder of ELK.Audio, demonstrating strong industry and innovation engagement. He leads multiple research initiatives and labs focused on networked machine learning, including groups working on federated learning, wireless AI, and edge intelligence. His team actively contributes to advancing the theoretical and practical foundations of machine learning over networks.
