Bio-Inspired Depth and Motion Sensing
Our computational models explain jumping spider behavior under defocus and spatial biases in larval zebrafish optic flow responses. They also enable a family of highly-efficient depth and velocity cameras [metalens, focal track, focal flow], reveal mathematical connections between depth photography and phase microscopy [TIE], and describe natural scene statistics of underwater motion [zebrafish].
Best Student Paper ECCV 2016, Best Demo ICCP 2018, patent licensed to Metalenz
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Machine Learning for Scientific Imaging
Physics-informed machine learning allows us to combine models of the physical world with data-driven priors to solve difficult inverse problems like galaxy shear estimation for cosmology and material classification from thermal video . A combination of machine learning and classic image processing techniques reveal more information from medical imaging modalities including X-ray, photoacoustic, and optical coherence tomography.