IEEE Photonics Society

Boston Photonics Society Chapter

Boston Chapter of the IEEE Photonics Society

Machine Learning and Optical Systems
-- The workshop is being postponed to October due to COVID-19 -- PDF

Wednesday, April 8, 15, 22, 29 and May 6, 2020, 7:00-9:30 PM
Located at MIT Lincoln Laboratory – 3 Forbes Road, Lexington, MA, 02420, USA

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.

April 8, 2020

Mapping AI Workloads to a Photonic Matrix Multiplier

Dr. Gilbert Hendry, Lightelligence, Boston, MA

Deep Elastic Strain Engineering of Band Structure through Machine Learning

Prof. Ju Li, Massachusetts Institute of Technology, Cambridge, MA

April 15, 2020

Advancing Optical Communication and Measurement Systems Using Machine Learning

Prof. Darko Zibar, Technical University of Denmark, Kongens Lyngby, Denmark

Interfacing Photonics with Artificial Intelligence: A New Design Strategy for Photonic Metamaterials based on Deep Learning

Prof. Yongmin Liu, Northeastern University, Boston, MA

April 22, 2020

Model-Based Machine Learning for Fiber-Optic Communication Systems

Prof. Henry D. Pfister, Duke University, Durham, NC

Towards Large-Scale Optical Neural Networks based on Quantum Photoelectric Multiplication

Dr. Ryan Hamerly, Massachusetts Institute of Technology, Cambridge, MA

April 29, 2020

Automatic Design of Optoelectronic Materials with Atomistic Simulations and Deep Learning

Prof. Rafael Gomez-Bombarelli, Massachusetts Institute of Technology, Cambridge, MA

Deep Learning of Ultrafast Pulses with a Multimode Fiber

Prof. Hui Cao, Yale University, New Haven, CT

May 6, 2020

Toward a Thinking Microscope: Deep Learning-enabled Computational Microscopy and Sensing

Prof. Aydogan Ozcan, University of California, Los Angeles, CA

Deep Learning-Based Approaches for Meta-Atom and Meta-Device Design

Prof. Hualiang Zhang, University of Massachusetts, Lowell, MA

Advance registration and fee required (Open to all IEEE members as well as non-members)

$50/$60 (IEEE Member/Non-Member) early registration fee for ten 1-hour talks over five nights; cost includes coffee and cookies each night, as well as downloadable copies of speakers slides. Early registration deadline March 18th, 2020. Post deadline fee $60/$70 (IEEE Member/Non-Member).

For more information on the technical content of the workshop, contact either:
1) Keisuke Kojima, (, Chair
2) Ajay Garg, (, Co-Chair
3) Dean Tsang, (, Co-Chair
4) Bill Nelson, (, Co-Chair