This $352,098 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new methods to ensure deep learning models for image reconstruction remain reliable and accurate even when the data conditions shift. The project, titled "COLLABORATIVE RESEARCH: CIF: MEDIUM: ROBUSTNESS TO DISTRIBUTION SHIFTS IN COMPUTATIONAL IMAGING - INFERENCE, SAMPLING, AND ADAPTATION", will introduce a unified mathematical framework called "Robust Score-based Inversion (ROSI)" to (i) quantify distribution shifts, (ii) characterize their impact on reconstruction and sampling performance, and (iii) enable principled adaptation of models to new imaging settings. The research will be validated across real-world imaging systems including lensless cameras, computational microscopes, and magnetic resonance imaging. The project also plans to promote education and engagement in computational imaging and machine learning. The award period runs from October 1, 2025 to September 30, 2029.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $352.1k | 7/11/25 |