Dr. Francesco Restuccia

Abstract: The massive scale and strict performance requirements of next-generation wireless networks will require embedded devices to perform real-time fine-grained optimization of their spectrum usage. Yet, today's networking protocols and architectures are deeply rooted in inflexible designs and utilize optimization models and strategies that are either too complex or too oversimplified to be fully effective in today's crowded spectrum environment. In this talk, we are going to introduce and discuss our recent research toward the design of self-adaptive learning-based wireless systems, where transmitters and receivers use real-time deep learning to infer and optimize their networking parameters based on ongoing spectrum conditions. We will conclude the talk by discussing existing challenges and future research directions in the field of real-time deep spectrum learning.

Biography:

Francesco Restuccia is an Assistant Professor with the Department of Electrical and Computer Engineering, Northeastern University, United States. Dr. Restuccia's research interests lie at the intersection of wireless networking, artificial intelligence, and embedded systems. Dr. Restuccia has published over 25 papers in top-tier venues such as IEEE INFOCOM, ACM MobiHoc, ACM SenSys and IEEE/ACM Transactions, as well as co-authoring 10 pending US patents and 2 book chapters. Dr. Restuccia regularly serves as a TPC member and reviewer for several ACM and IEEE conferences and journals. Dr. Restuccia's research on learning-based embedded wireless networking has been recognized with the inaugural 2019 Mario Gerla Award for Young Investigators in Computer Science by the Italian Scientists and Scholars of North America Foundation (ISSNAF). Dr. Restuccia is a Member of the IEEE and the ACM.