An Introduction to Machine Learning in Optical Networks

• Instructor

  Massimo Tornatore, Politecnico di Milano, Italy

• Day & Time

  19.09.2022, 13:30 – 17:30

• Location

  Room Hongkong

• Description

  Machine learning (ML) has recently attracted a surge of interest in optical networking and communication research due to its pattern recognition and predictive capabilities for various key applications. Large-scale monitoring data are generated every day in optical networks, which makes ML a promising solution for decision making. In this short course, we introduce the fundamental concepts and principles of ML. We survey existing work on various applications at the optical network level, focusing on fault management and quality of transmission estimation. Finally, we carry out a hands-on tutorial for participants showing how to implement a simple application of ML for fault management. We aim to provide a general overview of the key problems, common formulations, existing methodologies and future directions. This course will inspire the audience and facilitate ML research and development in optical networking and communication systems.

  The outline is given below:
    •  Fundamental concepts of ML
    • ML Applications
      –  Quality of Transmission
      –  Failure detection and identification
      –  Overview of other applications
    •  Hands-on activity

• Instructor Biopgraphy

  Massimo Tornatore is an Associate Professor at Politecnico di Milano, Italy. He also held 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 and design of communication networks (with an emphasis on optical networking), and machine learning application for network management. He co-authored more than 400 conference and journal papers (with 19 best-paper awards) and of the recent Springer “Handbook of Optical Networks”.  He is member of the Editorial Board of IEEE Communication Surveys and Tutorials, IEEE Communication Letters, IEEE Transactions on Network and Service Management and IEEE/ACM Transactions on Networking.

• Intended Audience

  The course is intended for interested people from academia and industry without any previous knowledge in machine learning. A basic understanding of optical fiber transmission and programming can be helpful but is not a hard requirement. Attendance is also beneficial for machine learning experts with limited optical networking background who want to learn about the potential applications in the area of optical communication and networking.

• Requirements

  Attendees should bring laptops with an Python installed.