Optical computing has resurged with a promise of enhancing the performance of conventional electronic systems in terms of energy efficiency, low latency and computation throughput by offering low-loss transmission, wide bandwidth, and various multiplexing schemes for parallel computation. We bring together researchers and professionals from academia and industry to discuss this workshop and investigate the prospective developments and positioning of optical computing.
The key questions to address in this workshop are:
- What does the future hold for optical computing?
- How should it be positioned with respect to electronic computing?
- Which applications will benefit the most from optical computing? (AI, communication, cryptography, quantum, etc.)
- Analog, digital, or hybrid systems? Coherent, non-coherent systems?
- Hardware-application co-design: application-specificity vs flexibility, what are the trade-offs?
- Integrated vs. bulk optics: Will there be a winner? What are the considerations for scale and form-factor?
Organizers
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Farshid Ashtiani
Nokia, United States
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Chaoran Huang
Chinese University of Hong Kong, Hong Kong
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Francesco Morichetti
Politecnico di Milano, Italy
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Patty Stabile
Technische Universiteit Eindhoven, Netherlands
Speakers
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Djamshid Damry
Lumai, United Kingdom
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Dirk Englund
MIT, United States
Dirk Englund is Professor of Electrical Engineering and Computer Science at MIT, where he leads research in quantum photonics, optical computing, and AI for science. His group develops integrated photonic systems for quantum information processing, neuromorphic computing, and sensing. He received his BS in Physics from Caltech and his MS and PhD from Stanford University. His recognitions include Fellow of IEEE and of Optica, the PECASE, Adolph Lomb Medal, and NSF CAREER Award. His research has contributed to several deep tech startups in optics, quantum computing, and AI.
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Lu Fang
Tsinghua University, China
Dr. Fang is Professor of Electronic Engineering at Tsinghua University, bridging physical optics and artificial intelligence to advance next-generation imaging and neuromorphic computing. She received her Ph.D. from the Hong Kong University of Science and Technology and B.E. from the University of Science and Technology of China. Her work has appeared in Science, Nature, and Nature Photonics, and she is a recipient of the Humboldt Research Fellowship for Experienced Researchers and the 2025 Falling Walls Science Breakthrough of the Year in Engineering & Technology.
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Ryan Hamerly
MIT, United States
In 2016 Ryan Hamerly received a Ph.D. degree in applied physics from Stanford University, California, for work with Prof. Hideo Mabuchi on quantum control, nanophotonics, and nonlinear optics. In 2017 he was at the National Institute of Informatics, Tokyo, Japan, working with Prof. Yoshihisa Yamamoto on quantum annealing and optical computing concepts, and is currently a Senior Scientist at NTT PHI Laboratories and a visiting scientist at MIT, Cambridge, Massachusetts, with Prof. Dirk Englund, as well as an advisor and co-founder of the computing startup Opticore.
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Aydogan Ozcan
UCLA, United States
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Francesca Parmigiani
Microsoft, United Kingdom
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Bhavin Shastri
Queen's University at Kingston, Canada
Prof. Shastri is a Canada Research Chair and Associate Professor at Queen’s University, Canada. A Member of the College of the Royal Society of Canada, he also serves as Scientific Co-Director of NUCLEUS, a pan-Canadian program advancing photonic computing. He is a 2025 Alfred P. Sloan Research Fellow in Physics, the recipient of the 2022 SPIE Early Career Achievement Award, and the 2020 Young Scientist Prize in Optics from the International Commission of Optics (ICO) “for his pioneering work in neuromorphic photonics.” He co-authored Neuromorphic Photonics (Taylor & Francis, 2017), a term he co-coined.
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Logan Wright
Yale, United States
Logan Wright is an assistant professor of applied physics at Yale University. His research group is focused on the implementation of computationally intensive methods like optimization and machine learning with and for complex photonic systems.