معرفی
Bhavin J. Shastri is an Assistant Professor in the Department of Physics, Engineering Physics and Astronomy at Queen's University in Canada. His research explores the physics of light for computing, pushing frontiers in information and signal processing through photonic computing and quantum/neuromorphic photonics. He is affiliated with the Centre for Nanophotonics and NUCLEUS, a pan-Canadian photonic computing program funded by NSERC CREATE, bridging artificial intelligence and quantum information.
- Canada Research Chair & Principal Investigator
- Faculty Affiliate at Vector Institute (2020-)
- Editorial Board Member of JPhys Photonics (2019-)
- Member of IEEE Photonics Society Technical Affairs Council (2019-)
- Visiting Researcher Scholar at Princeton University (2018-)
Shastri Lab members have access to world-class shared facilities, including the Centre for Nanophotonics (CFI-Innovation Fund), Nanofabrication Kingston, the Centre for Advanced Computing, and the Digital Research Alliance of Canada. The lab takes an interdisciplinary approach combining nanophotonics with complex systems on emerging substrates.
His research focuses on silicon photonics, nanophonic processors, and photonic integrated circuits with applications to deep learning, nonlinear programming, and quantum information science. His articles show consistent exploration of quantum photonic neural networks, photonic memory systems, and optical signal processing for machine learning and quantum technologies.
- 2020 IUPAP Young Scientist Prize in Optics
- 2014 Banting Postdoctoral Fellowship
- 2012 D. W. Ambridge Prize
- 2011 IEEE Photonics Society Graduate Student Fellowship
- 2011 NSERC Postdoctoral Fellowship
- Multiple Best Student Paper Awards
Shastri's lab supervises Ph.D. candidates and postdoctoral fellows working on quantum photonics, neuromorphic computing, and photonic AI systems. His recent work includes photonic tensor cores for scientific computing, quantum photonic neural networks, and all-optical memory systems.
Shastri Lab designs programmable nanophotonic processors with potential to outperform microelectronic processors in energy efficiency and computational speeds by seven and four orders of magnitude respectively. Their work spans from device design to system-level implementations in optical computing for machine learning and quantum information processing.
