This workshop will feature talks on different aspects of the interaction between Machine learning (ML) and optical systems. This interplay, between how ML is used to improve optical systems and how optical systems are used to implement the deep neural network hardware (DNN) for ML, is one of the driving forces advancing optics applications.

ML including DNN provide a new set of tools to the photonics and optical communication community, which offer opportunities when the system is highly complex, or when there is a lack of analytical models. Recent applications of ML to optical materials, optical design, plasmonics, metasurface optics, optical communication systems, and optical measurements will be covered and will offer insights as to how various optical problems can benefit from ML.

Photons are ideal information carriers in distributed processors such as DNNs and quantum computers. There are a variety of optical approaches to accelerate DNNs and quantum computers, and significant effort has been made towards prototyping such systems. Some implementations offer massive parallelism, while others offer flexibility in a compact size. Examining these differences will show us how photonics could play a role in future computing.

This workshop will bring together leading experts to discuss the latest research in the intersecting fields of ML, DNN, photonics, and optical communications. It also aims to foster communication and collaboration through networking among the individual engineers and researchers attending. Learn more about the rapid advances in the application of: ML to photonics and optical communications; and the optical implementation of neural network computing, directly from the foremost researchers in the different specialties involved, by registering for and attending this workshop.