Computational Imaging Lab @ Cornell

We combine ideas from machine learning, signal processing, optics, computer vision and physics to build better imaging systems (cameras, microscopes, and telescopes) through the co-design of optics, algorithms, and high-level tasks. Our aim is to design the next generation of smart, computational imagers that fuel scientific discovery, robotics, and medical diagnostics. We are particularly interested in:

• Differentiable optics - Can we use data and machine learning tools to design better cameras, microscopes, and telescopes?
• Physics-informed machine learning - How can we effectively combine our knowledge of imaging system physics with deep learning?
• Task-based imaging systems - What's the best camera or microscope for high-level tasks, such as robotics or medical diagnostics?
• Inverse problems and neural representations: Can we leverage neural priors to improve our imaging systems for microscopy and photography?

Check out our previous research projects and publications for more information!