Machine Learning Photonics
14 – 18 September 2020
The school brings together experts in emerging photonic technologies, machine learning techniques, and fundamental physics who will share with young researchers their knowledge and interdisciplinary approaches for understanding and designing complex photonic systems and their practical applications. In the new era of artificial intelligence, algorithms and computational interfaces are broadly emerging as novel tools to do scientific research. The paradigms of machine learning also inspire interpretations and methodologies, in both theories and experiments. Nonlinear, quantum and bio-photonics, as well as optical communications, are surprisingly influenced by these new ideas. The summer school is aimed to explore machine learning applications in the specific fields of nonlinear optics and photonics.
The areas covered include, but are not limited to: machine learning methods and complexity of optical communication systems, including topics such as the nonlinear Fourier transform and transmission over multimode fibres; complexity in quantum systems emulated in photonics (including optical computing); complexity of emerging novel materials, device and components such as micro-resonators and plasmonic nanostructures. Importantly, the complexity in bio-medical photonic applications will be also considered as a high priority topic.
The summer school will focus on comprehensive review talks from major figures in complementary areas of photonics and machine learning. Invited speakers will deliver one or more 1-hour lectures over 5 days. We shall select up to 30 participants from the pool of most vibrant PhD students and postdocs, with a special focus on Marie Curie Fellows as future leaders of European photonics science and industry.