Prof. David Saad
David Saad holds the 50th Anniversary Chair of Complexity Physics and is Head of Mathematics at Aston University, Birmingham UK. He holds degrees in Physics and Electrical Engineering from the Technion, Haifa, Israel and Tel-Aviv University. He joined the Physics Department at Edinburgh University in 1992 and Aston University in 1995. His research, published in about 200 journal and conference papers, focuses on the application of methods from statistical physics and Bayesian statistics to a range of fields, which include neural networks, error-correcting codes, multi-node communication, network optimisation, routing, noisy computation, epidemic spreading and advanced inference methods.
Machine learning for photonics
Machine learning is a colloquial term that encompasses a collection of data-driven methods of various types aimed at understanding, inferring and optimising systems. This includes inferring the state of individual system variables, the interaction strengths between them and the system’s characteristic phases. The recent excitement around engineering successes of machine learning led to their application in tackling fundamental questions in various aspects of photonics, especially the interpretation and inference of experimental data. These successes suggest that machine learning techniques may become a standard tool in physics  and photonics  research. In this talk I will review existing machine learning techniques and motivate the use of principled probabilistic approaches, while explaining recent high-profile heuristics and their limitations. Additionally, I will talk about specific photonics application that could benefit from the use of machine learning techniques.
 L. Zdeborová, Nature Physics 13, 420EP (2017)
 D. Zibar, M. Piels, R. Jones and C. G. Schäeffer, J. of Lightwave Tech., 34(6), 1442 (2016)