Symposium Organizers
Zongfu Yu, Univ. of Wisconsin Madison, USA
Darko Zibar, Technical Univ. of Denmark, Denmark
Shanhui Fan, Stanford University, USA
Bahram Jalali, Univ. of California Los Angeles, USA
Marin Soljacic, Massachusetts Institute of Technology, USA
Over the past 5 years, tremendous progress has been made in machine learning. Its impact have started to emerge across a broad range of fields. Photonics is one of them. This symposium will highlight recent progress at the intersection of photonics and machine learning. Various methods such as deep learning, Bayesian inference, Monte Carlo Markov Chain and Gaussian processes will be addresses on how they can provide new paths for solving the most critical problems in various fields in photonics. For example, deep learning points to new inverse design approach for complex photonic structures while Bayesian inference offers detection methods that can operate at the quantum limit. Unlike optimization-driven approaches that require expensive computation, machine learning leverages on learning form the data. Photonics also provides exciting opportunities for all optical implementation of various machine learning techniques. Analog neural computing with photonic chips could improve energy efficiency and speed by orders of magnitude. There are also many other exciting developments in microscopy, quantum communication, sensing, bio-medical image recognition, optical communication and opto-mechanics that have benefited from machine learning.