This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) for $299,354 provides funding to develop a novel learning-driven framework to mitigate artifacts produced by scientific data compressors. The project aims to improve the integrity and quality of lossy-compressed scientific data, facilitating more efficient data storage, transmission, and analytics across domains including climatology, cosmology, fusion energy science, and X-ray ptychography. The project leverages recent advancements in scientific data compression and deep learning to characterize compression artifacts, design models for artifact mitigation, and validate the quality of recovered data. This framework is expected to be integrated into state-of-the-art error-controlled lossy compressors and incorporated into real-world scientific applications. The award was made to The University of Kentucky Research Foundation, with a performance period from October 1, 2025 to September 30, 2028.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $299.4k | 4/3/25 |