- Nathan Kutz, University of Washington, USA: Machine Learning for Physics Discovery and Control in Optical Systems
- Demetri Psaltis, EPFL, Switzerland: Machine learning in imaging in complex optical media
- Serge Massar, ULB Bruxelles, Belgium: Towards high-performance spatially parallel optical reservoir computing
- Goery Genty, Tampere University, Finland: Machine learning analysis of extreme events in optical modulational instability
- Darko Zibar, DTU, Denmark: Machine learning techniques for optical communications
- Shuangyi Yan, University of Bristol, UK: Machine-learning applications in future optical networks
- Stéphane Barland, Institut Non Linéaire de Nice, France: Resonator neuron and triggering multipulse excitability in laser with injected signal
- Diederik Wiersma, LENS Lab, Italy, Nanophotonics-based micro robotics
- Lorenzo Pavesi, Università di Trento, Italy: Relationship between brain connectivity and function by integrated photonics
- Ingo Fisher, CSIC-UIB, Spain: Ultrafast photonic reservoir computing: from fundamental properties to real-world applications
- P.T. Lau, The Hong Kong Poly University, Hong Kong: Machine learning applications in optical Communications and Networks
- Benjamin Wetzel, XLIM France: Nonlinear guided optics & applications: from ultrashort pulse processing to multidimensional control
- Claudio Conti, CNR-ISC, Italy: Neuromorphic computing and Ising machines by wave
- Daniel Brunner, Institut Femto, France: How to do reservoir computing with photonic systems; 3D photonic integration for scalable photonic neural networks
- Mathieu Chagnon, Nokia Bell Labs, Germany: Reinventing Communication Methods with Machine Learning: From Bits-in to Bits-out
- Jakob Hoydis, Nokia Bell Labs, France ML in wireless communications
- David Saad, Aston University, UK: Machine learning beyond the hype – principled methods for photonics
- Aurelio Uncini, Sapienza University of Rome: Deep Neural Networks: New Trends and Perspective for Big-Data Applications