• Technical Conference:  5 – 10 May 2019
  • Exhibition: 7 – 9 May 2019

Machine Learning Photons: Where Machine Learning and Photonics Intersect

Symposium Organizers

Zongfu Yu, University of Wisconsin Madison, USA

Darko Zibar, Danmarks Tekniske Universitet, Denmark

Shanhui Fan, Stanford University, USA

Bahram Jalali, University of California Los Angeles, USA

Marin Soljačić, Massachusetts Institute of Technology, USA


Over the past 5 years, tremendous progress has been made in machine learning. Its impact has 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. Combination of deep learning with time stretched measurements has been highly successful in biological cell analysis at extreme throughput. 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. 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.


Invited Speakers

Wenshan Cai, Georgia Institute of Technology, USA

Hou-Man Chin, Danmarks Tekniske Universitet, Denmark

Folkert Horst, International Business Machines Corp., Switzerland

Tyler Hughes, Stanford University, USA

Ata Mahjoubfar, University of California Los Angeles, USA

Aydogan Ozcan, University of California Los Angeles, USA

Paul Prucnal, Princeton University, USA

Marin Soljačić, Massachusetts Institute of Technology, USA

Ken Xingze Wang, Huazhong Univ of Science and Technology, China

Tom Zahavy, Technion, Israel    

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