Project Grant 2545314
- The National Science Foundation's Computer and Information Science and Engineering (CISE) Federal Grant Program has awarded $299,354 to The University Of Kentucky Research Foundation for the project "COLLABORATIVE RESEARCH: OAC CORE: MITIGATING ARTIFACTS IN SCIENTIFIC DATA COMPRESSORS WITH A LEARNING-DRIVEN FRAMEWORK." This Project Grant, running from October 1, 2025 to September 30, 2028, aims to develop a novel learning-driven framework to mitigate artifacts produced by scientific...
- This $299,999 Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with an award date of July 1, 2025 and an ultimate completion date of June 30, 2028. The grant aims to develop a cyberinfrastructure that seamlessly integrates lossy compression techniques into deep learning pipelines for scientific applications, in order to reduce memory usage and communication overhead and enable AI-for-science...
- This $199,989 project grant, awarded by the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to research and develop advanced lossy data compression techniques that preserve topological features in large-scale scientific data. The project at The Ohio State University will tackle the data compression, analysis, and visualization needs of extreme-scale scientific simulations by creating a...
- This $198,234 Project Grant awarded by the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure (CFDA 47.070 - Computer and Information Science and Engineering) supports research and development of advanced lossy data compression techniques that preserve topological features in scientific data. The project aims to develop algorithms to effectively reduce the size of large-scale scientific simulation data, such as from fusion and climate modeling, while preserving critical...
- The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to North Carolina State University (NC State) to develop a cyberinfrastructure that seamlessly integrates lossy compression techniques into deep learning pipelines for scientific applications. The overarching goal is to enable AI-driven scientific applications to effectively scale to massive datasets by reducing memory usage and...
- This Project Grant from the National Science Foundation's Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $203,483 to Indiana University to develop an objective-driven adaptive hybrid lossy compression framework for extreme-scale scientific applications. The framework aims to automatically construct the best-fit compression strategy for diverse user objectives in data-intensive scientific research....
- This National Science Foundation (NSF) Project Grant award for $599,999 supports research at Cornell University from June 15, 2023 to May 31, 2026. The objective is to develop a "modern theory of data compression" to explain the performance of artificial neural network-based compression algorithms and identify avenues for future improvements. The research aims to advance the field of data compression, which has important implications for enabling more realistic and immersive...
- This $819,000 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) will fund a collaborative research project titled "SCIOPT: Toward Certifiable Compression-Aware SCIML Systems" at the University of Utah. The project aims to develop techniques to reduce the volume of data exchanged in high-performance scientific simulations and scientific machine learning (SCIML) applications without sacrificing accuracy. Key...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) Project Grant award of $272,992 will support a collaborative research project at Texas State University titled "SCIOPT: Toward Certifiable Compression-Aware SCIML Systems." The project aims to develop techniques to reduce the volume of data exchanged in high-performance scientific computing and scientific machine learning (SCIML) applications without...
- This $540,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a comprehensive framework called FZ to enable scientific users to intuitively research, compose, implement, and test specialized lossy data compression techniques. The project will build on existing capabilities from various open-source data compression tools to create an intuitive cyberinfrastructure for the composition of specialized...
This Project Grant award of $299,999.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to develop a novel learning-driven framework to mitigate artifacts produced by scientific data compressors. The project, led by Miami University, seeks to improve the integrity and quality of lossy-compressed scientific data, enabling more efficient data storage, transmission, and analytics across various scientific domains such as climatology, cosmology, fusion energy science, and X-ray ptychography. Key objectives include characterizing compression artifacts, designing deep learning models for artifact mitigation, and validating the quality of recovered data through uncertainty quantification. The project's success is expected to facilitate scientific discoveries and enhance research and education in advanced cyberinfrastructure. The award period runs from October 1, 2025, to December 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 8/14/25 |