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Bio
I developed mathematical and numerical techniques for inverse design and optimization of photonic structures based on the 'adjoint variable method' (AVM). This technique allows one to efficiently compute how a device's performance will change with respect to a change in numerous design parameters using only a single additional simulation. This information is incredibly useful and can be used to perform sensitivity analysis or gradient based optimization. Many of my papers extend this method and apply it to large-scale optimization of various photonic devices. I have also drawn connections between the mathematics of the technique and physical processes occuring in photonic devices, using these connections to come up with new ways to train optical machine learning hardware and build analog recurrent neural networks using waves. As part of my PhD work, I was also involved in the Accelerator on a Chip International Program (ACHIP), which is working towards building miniature particle accelerators on a chip using advanced laser and nanofabrication technology (video explanation). I have been heavily involved in the scaling of these accelerators using integrated photonic circuits and control methods built directly onto the chip.
Research Interests
Papers共 79 篇Author StatisticsCo-AuthorSimilar Experts
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The Lancetno. 10433 (2024): 1254-1266
2023 IEEE PHOTONICS CONFERENCE, IPCpp.1-2, (2023)
Nature Physicsno. 9 (2023): 1308-1313
Research for Allno. 1 (2023): 13
Advances in Optics and Photonicsno. 4 (2022): 862-930
Conference on Lasers and Electro-Opticspp.1-2, (2022)
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