Zongfu Yu, Univ. of Wisconsin Madison, USA
Darko Zibar, Technical University of Denmark, Denmark
Over the past 5 years, tremendous progress has been made in deep learning. Its impact starts 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 deep learning. For example, deep learning points to new inverse design approach for complex photonic structures. Unlike optimization-driven approaches that require expensive computation, machine learning leverages big data to realize fast inverse design. Photonics also provides exciting opportunities for deep learning. Analog neural computing with photonic chips could improve energy efficiency and speed by orders of magnitude. There are many other exciting developments in microscopy, quantum communication, sensing, bio-medical image recognition, optical communication and opto-mechanics.