This Project Grant award of $381,347.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to develop new methods for ensuring that deep learning models for image reconstruction remain reliable and accurate even when the data conditions shift. The central goals are to (i) quantify the extent of distribution shifts between training and test data, (ii) characterize the effect of shifts on reconstruction and sampling performance, and (iii) enable principled adaptation of models to new imaging settings. The research will be validated in real-world imaging systems including lensless cameras, computational microscopes, and magnetic resonance imaging, providing both theoretical insights and practical tools for reliable computational imaging. The project, awarded to the Regents of the University of California at Riverside, runs from October 1, 2025 through September 30, 2029.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $381.3k | 7/11/25 |