Abstract : Artificial intelligence (AI) powered by neural networks has enabled applications in many fields (medicine, finance, autonomous vehicles). Digital implementations of neural networks are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors that use light and photonic device physics to mimic neurons and synapses in the brain for distributed and parallel processing while offering sub-nanosecond latencies and extending the domain of AI and neuromorphic computing applications. We will discuss photonic neural networks enabled by CMOS-compatible silicon photonics. We will highlight applications that require low latency and high bandwidth, including wideband radio-frequency signal processing, fiber-optic communications, and nonlinear programming (solving optimization problems). 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. Speaker(s): Prof. Bhavin Shastri, Bldg: McConnell Engineering building, , Room MD 497, 4th floor, 817 Sherbrooke St W, , Montreal, Quebec, Canada, H3A 0C3