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A series of talks from experts in optics and photonics
In a world where Moore's law is encountering the physical limits and electronic circuits, after six decades of development, face formidable barriers, the pursuit of novel computing paradigms has become paramount. Among these frontiers, optical and photonic systems have emerged as a driving force. Yet, they present a unique challenge – the complexity of understanding optical/photonic systems, governed by Maxwell's equations, surpasses that of electronic circuits.
This is where our seminar series steps in! We have assembled some of the brightest minds from both industry and academia to bring the cutting-edge field of optical/photonic systems to your doorstep. Whether you are an enthusiastic junior student seeking to explore the wonders of this domain or a seasoned faculty member aiming to delve deeper, our seminars offer an ideal platform. Expect dynamic discussions, inspiration, and the potential for fruitful collaborations. Join us in shaping the future of computing. The future has arrived, and it's illuminated by optics!
To stay updated as an audience member, please subscribe to our mailing list. If you're interested in presenting, please reach out to us at optsys23@gmail.com. Explore our archive of past seminars, complete with recorded videos and presentation slides, to see the history of our engaging sessions.
Upcoming Seminar
Upcoming Seminar
Title: Optical Machine Learning with Complex Optics
Abstract: Complex optics refers to light propagation in disordered, highly scattering structures which naturally mixes vast optical modes, with potential for high-dimensional, passive, and massively parallel transforms at optical bandwidths. This makes complex optics an interesting platform to explore optical machine learning. Here, we report two advances along this direction trying to address two key challenges in optical machine learning: nonlinearity and scaling. To introduce nonlinearity in optical computing, we leverage a multiple-scattering cavity that provides structural nonlinearity, acting as a parallel optical encoder that maps inputs into rich nonlinear optical feature spaces without electronic overhead. To introduce larger scale computing, we introduce a reconfigurable, trainable optical neural networks use complex media as dense optical weights; with in-situ (hardware-in-the-loop) training, we directly optimize the system’s transfer function, reducing calibration and scaling naturally with aperture/mode count. Their computing performances are evaluated on industry and scientific datasets and show potential unconventional paths towards optical machine learning.
Biography: Fei is an Assistant Professor of Electrical Engineering and Computer Science (EECS) at the University of California, Irvine.She leads a research team working at the interface of optics, computation, and the brain, with a particular interest in designing advanced optical systems to image, sense, and process biomedical information probed by light. She aims to provide innovative solutions to challenging biomedical needs through the co-design of hardware and software. Fei has been recognized by several awards and honors, including the Scialog Fellow for RCSA Initiative, Rising Star in Light, Optica Foundation Challenge Award, Seal of Excellence for Marie Skłodowska-Curie Fellowship, the SPIE Women in Optics, Data Open Championship and the Mong NeuroTech Fellowship.
Date and Time: Oct 10, 2025, 9:30 am - 10:30 am (U.S. Pacific Time)
Zoom Link: https://mit.zoom.us/j/96986397494
Organizers
Acknowledgment
This seminar was made possible through the generous contributions of our presenters and the active participation of our audience. Particualrly, we would like to extend our gratitude to the following individuals for their invaluable help in refining this seminar and making it accessible to a wider audience: Hanrui Wang.
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