AI and Optical Data Sciences VI

The exponential increase in the amount of data created every day has led to a new era in data exploration. This demands novel compute and data processing paradigms including co-design, hardware-algorithm optimization, and machine- and deep learning approaches. Optical sensors and fibers have enabled the capture and transfer of massive data across both short and long distances and have formed the backbone of the internet. Data-center traffic is dominated by machine learning training tasks driving by larger models and structured data. Furthermore, space technology, e-mobility and autonomous vehicles are driving the need for network edge intelligence. Lastly, the field of biological research and healthcare have been transformed by photonics sensing technologies ranging from imaging, tomography to spectroscopy. These trends are fueling the need and the opportunity for artificial intelligence (AI) techniques that take advantage of the massive amount of data routed through optical fibers of data-centers and the network edge, are generated by analog sensors and from IoT and metrology instruments alike. Going forward, the convergence of AI with cutting-edge optics and photonics will have a transformative impact on communication, imaging, sensing, and AR/VR systems, etc. With the advent of photonic integrated circuits, the miniaturization and synergistic design with electronics allows for More-than-Moore machine learning architectures.

The goal of this conference is to serve as a unique platform for bringing together artificial intelligence and photonics researchers from around the world to showcase the newest trends and best practices in the field. Researchers from leading companies and universities present their high-impact research and products and exchange new ideas.

Topics of interest include but are not limited to:

• Photonic hardware accelerators
• Novel photonic devices for machine learning
• Heterogenous Integration of PICs
• Advanced packaging of PICs
• Analog optical computing
• Physics-inspired machine learning algorithms
• Physics-AI symbiosis
• Inverse design of metamaterials via machine learning
• Computational imaging
• Optical classification and real-time inference
• Compressed sensing
• Imaging and spectroscopy
• Optical encryption and security
• Mobile edge computing
• Photonic reservoir computing
• Reinforcement learning based on physical phenomena
• Augmented reality and virtual reality
• Time stretch instruments.