Welcome to the Computational Imaging Group (CIG)!

Computational Imaging is a rapidly growing research area at the intersection of data science, computer vision, machine learning, applied mathematics, and physical sciences. Computational Imaging Group (CIG) develops advanced algorithms for fast and reliable acquisition, efficient processing, and automated analysis of imaging data, including 2D, 3D, or 4D images and videos. Applications include biomedical imaging, material sciences, geospatial sensing, space exploration, industrial inspection, and consumer electronics. We develop algorithms and theoretical foundations for enabling advanced capabilities in future imaging and sensing systems. We have expertise in deep learning, large-scale optimization, signal and image processing, computer vision, and statistical inference. Our research is inherently interdisciplinary and includes collaborations with researchers in optics, medicine, physics, materials, and biology.

Interested in doing research in CIG? Multiple opportunities at all levels.


Latest News

August 2022: Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging was accepted to IEEE Signal Processing Magazine.

August 2022: Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields was accepted to Nature Machine Intelligence.

August 2022: The slides of the CIG talk at Google Computational Imaging Workshop are here.

July 2022: New paper Deep Model-Based Architectures for Inverse Problems under Mismatched Priors.

July 2022: Congratulations to Dr. Xiaojian Xu for defending her PhD thesis. Xiaojian will be moving to University of Michigan as a postdoctoral researcher.

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