Advancing Photonic Device Design and Quantum Measurements with Machine Learning
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Abstract
Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements.
Bio
Alexandra Boltasseva is a Professor at the School of Electrical & Computer Engineering and Birck Nanotechnology Center, Purdue University. She received her PhD in electrical engineering at Technical University of Denmark, DTU in 2004. Boltasseva specializes in nanophotonics, nanofabrication, optical materials, plasmonics and metamaterials. She is 2018 Blavatnik National Award for Young Scientist Finalist, and received the 2013 IEEE Photonics Society Young Investigator Award, 2013 Materials Research Society (MRS) Outstanding Young Investigator Award, the MIT Technology Review Top Young Innovator (TR35) award that "honors 35 innovators under 35 each year whose work promises to change the world", the Purdue College of Engineering Early Career Research Award, the Young Researcher Award in Advanced Optical Technologies from the University of Erlangen-Nuremberg, Germany, and the Young Elite-Researcher Award from the Danish Council for Independent Research. She is a Fellow of the Optical Society of America (OSA) and SPIE. Alexandra authored more than 130 journal articles with a total number of citations above 11000. She served on MRS Board of Directors and is Editor-in-Chief for OSA’s Optical Materials Express.
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