Solid State & Optics Seminar Series
sponsored by “The Flint Fund Series on Quantum Devices and Nanostructures”
Wednesday, March 23, 2022
1:00pm
Via Zoom: https://yale.zoom.us/j/94720154098?pwd=N1hvZUQvWng2a2lZdndVVldDT1FzQT09 (passcode: 604783)
Logan Wright, Ph.D
Postdoctoral Research Scientist, Applied Physics, Cornell University
Deep physical neural networks: training physical systems like neural networks
Deep learning has proven to be a remarkably versatile and scalable technique for learning algorithms to process and interact with noisy, high-dimensional real-world data and systems. In deep learning, the backpropagation algorithm is used to adjust the parameters of a multi-layer (deep) neural network so that the network “learns” to perform desired mathematical functions. Here, I will discuss my work to adapt this procedure to train networks of controllable physical systems – physical neural networks (PNNs) - which directly learn physical functions, such as performing machine learning inference calculations. I will present proof-of-concept PNNs we have constructed to perform image and audio classification, based on ultrafast nonlinear photonics, bulk analog electronics, and mechanics. Because PNNs learn physical transformations directly, without relying on rigid mathematical isomorphisms, they may harness noisy, analog physical processes for computation more opportunistically than traditional approaches. For example, PNNs based on nonlinear optical waves or microwave oscillators offer routes to performing machine learning calculations millions of times faster and more energy-efficiently than conventional hardware. More broadly, PNNs form the basis for a learning-based approach to the design and programming of complex physical devices, such as optical sensors that perform ultrafast signal processing to drastically reduce the time, dose, and cost of biomedical diagnostics.
Bio:
Logan G. Wright is a postdoctoral research scientist at Cornell University and NTT Research. His research focuses on physical information processing, primarily with nonlinear and quantum optical systems. He received his PhD from Cornell University, where he studied ultrafast laser systems and multimode nonlinear optical waves. He is the recipient of several awards, including the Tingye Li Innovation Prize in 2018, and the Cornell William Nichols Findley award in 2015 and 2018.