His research lies at the interdisciplinary interface of nanophotonics, neuromorphic computing, and silicon photonic platform, an area that promises to open new frontiers in machine learning for artificial intelligence and brain-inspired computing.
His neuromorphic photonic processors have the potential to be six orders of magnitude faster than state-of-the-art electronic processors while consuming less energy. Applications include enabling fundamental physics breakthroughs qubit readout classification, high-energy particle collision classification ; nonlinear programing solving nonlinear optimization problems and partial differential equations ; scientific computing many-body physics, protein folding simulations ; and intelligent signal processing wideband RF, fiber-optic communication, spectral mining.
He has published more than 50 journal articles and 70 conference proceedings as well as giving more than 35 invited talks and lectures. Through Queen's University's Women in Science and Engineering organization, Shastri serves as a mentor to undergraduate and graduate students as well as offering research opportunities in his lab.
He and his students actively participate in outreach initiatives of the Aboriginal Access to Engineering initiative which encourages Aboriginal youth to consider STEM as a potential career path. Photonics — neuromorphic photonics in particular — is a formidable candidate for analog reconfigurable processing. I expect the development of this field to accelerate as neuroscience makes leaps in our understanding of the nature of cognition, as artificial intelligence demands more computational resources for machine learning, and as photonics technology continues to mature.
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