Events

[]The panel, titled “Leadership at the Intersection of AI and Silicon Photonics,” will bring together leading experts to discuss leadership across academia and industry, the role of AI in next-generation photonics technologies, and strategies for career development and professional <a href="http://transitions.Speaker(s):" target="_blank" title="transitions.Speaker(s):">transitions.Speaker(s): Dr. Aref Chowdhury, Dr. Aaron Zilkie, Prof. Winnie Ye, Prof. Michal Lipson525 Legget Dr,, Kanata, Ontario, Canada, K2K 3G4

Low-Earth orbiting (LEO) satellites are now providing broadband service to users around the world. But they face space congestion problems. Some satellites must steer around each other to avoid collisions. In addition, the LEO satellites must share radio spectrum with geosynchronous Earth-orbiting (GEO) satellites and, more interestingly, with each <a href="http://other.This" target="_blank" title="other.This">other.This presentation will touch on collision avoidance but will focus on beam steering and other ways that these satellites can efficiently share spectrum with each other. There are a variety of ways they can do this. Some require information sharing, but others do not. Our work at Carnegie Mellon is examining the effectiveness of various spectrum sharing <a href="http://methods.Co-sponsored" target="_blank" title="methods.Co-sponsored">methods.Co-sponsored by: Lin CaiRoom: 660, Bldg: ECS, University of Victoria, Victoria, British Columbia, Canada, V8P 5C2

Abstract:Artificial intelligence and combinatorial optimization problems—such as drug discovery and prime factorization—remain challenging even for advanced computers. We are attempting to address these limitations by building photonic processors inspired by the brain—photonic neural networks—which utilize light for faster and more energy-efficient processing . We will discuss photonic networks, including Ising machines enabled by thin-film lithium niobate photonics , highlighting their applications in number partitioning, protein folding, wireless communications, and deep learning. Time permitting, we will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities . Shastri, B.J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15 (2021) Al-Kayed, N. et al. Programmable 200 GOPS Hopfield-inspired photonic Ising machine. Nature 648 (2025) Ewaniuk, J et al. Imperfect quantum photonic neural networks. Advanced Quantum Technologies (2023) .Co-sponsored by: Prof. Nicolas QuesadaSpeaker(s): Bhavin J. ShastriJ. Armand Bombardier J-1035, Polytechnique Montréal, Montréal, Quebec, Canada, H3T 1J4