Yair Rivenson
Papers
1
Total Citations
163
H-Index
1
About
Yair Rivenson is a leading figure in computational imaging and deep learning-driven optics, whose work bridges the gap between physical optics and artificial intelligence. His research focuses on developing novel imaging systems that leverage deep neural networks to bypass traditional hardware limitations, enabling breakthroughs in microscopy, holography, and all-optical image processing. One of his most impactful contributions is the demonstration of "computational imaging without a computer," where he showed that a deep learning-designed diffractive optical network can reconstruct images through random diffusers at the speed of light—eliminating the need for digital post-processing. This work, published in 2022 and already cited over 160 times, exemplifies his ability to merge optical hardware with machine learning for real-time, energy-efficient imaging. Rivenson’s broader portfolio includes pioneering methods in lensless imaging and quantitative phase microscopy, with his papers collectively amassing thousands of citations. His achievements have been recognized with multiple awards, and his research continues to inspire new directions in label-free biomedical imaging and intelligent optical systems.
Research Focus
Key Achievements
Top Papers
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