[WG3] Machine Learning in Optical Transport Networks

[WG3-1] Machine Learning Applications in Optical Networks

Tutorial

〇Massimo Tornatore (Politecnico di Milano, Italy)

After a generic introduction to machine-learning concepts and tools, several applications of machine learning in optical networks are introduced and discussed, with a focus on QoT estimation, failure detection and failure identification.

Bio:Massimo Tornatore (S’03–M’06–SM’13) is currently an Associate Professor at Politecnico di Milano, Italy, where he received his Ph.D. degree in 2006. He also holds an appointment as Adjunct Professor at University of California, Davis, USA and as visiting professor at University of Waterloo, Canada.
His research interests include performance evaluation, optimization and design of communication networks (with an emphasis on the application of optical networking technologies), cloud computing, and machine learning application for network management. In these areas, he co-authored more than 300 peer-reviewed conference and journal papers (with 13 best paper awards), 2 books and 1 patent. He is a member of the Editorial Board of IEEE Communication Surveys and Tutorials, IEEE Communication Letters, Springer Photonic Network Communications, and Elsevier Optical Switching and Networking. He is active member of the technical program committee of various networking conferences such as INFOCOM, OFC, ICC, and GLOBECOM.