Scalable photonics: an optimized approach
Lecture available on YouTube
Classical and quantum photonics with superior properties can be implemented in a variety of old (silicon, silicon nitride) and new (silicon carbide, diamond) photonic materials by combining state of the art optimization and machine learning techniques (photonics inverse design) with new fabrication approaches. In addition to making photonics more robust to errors in fabrication and temperature, more compact, and more efficient, this approach is also crucial for enabling new photonics applications, such as on chip laser driven particle accelerators, and semiconductor quantum simulators.
Jelena Vuckovic is a professor at Stanford, where she leads the Nanoscale and Quantum Photonics Lab. She is also the director of Q-FARM: the Stanford-SLAC Quantum Initiative. Vuckovic has won numerous awards including the IET AF Harvey Prize, James P. Gordon Memorial Speakership from the OSA, Humboldt Prize, the Distinguished Scholar Award from the Max Planck Institute for Quantum Optics, Hans Fischer Senior Fellowship, the Presidential Early Career Award for Scientists and Engineers, and the Young Investigator Awards from DARPA and the Office of Naval Research. She is a Fellow of the American Physical Society (APS), of the Optical Society of America (OSA), and of the Institute of Electronics and Electrical Engineers (IEEE).
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