This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $121,584 to the University of Texas at Austin to advance distributed data compression and communication technologies. The key objectives are to: 1) develop innovative frameworks for constructing compression and communication algorithms that leverage insights from information theory, generative models, and deep learning; 2) establish...
This National Science Foundation (NSF) Project Grant awarded under the Computer and Information Science and Engineering program (CFDA 47.070) provides $399,998 to the University of Hawaii at Manoa to support collaborative research on developing fundamental theory and methods for learning-based lossless and lossy data compression. The research aims to understand how machine learning algorithms can be used to compress data more efficiently, with applications in reducing wireless spectrum usage and...
This $200,000 Project Grant award from the National Science Foundation (NSF) Division of Computing and Communication Foundations, under CFDA 47.070 "Computer and Information Science and Engineering", funds research to develop fundamental theory and performance bounds for using machine learning approaches to enable more efficient data compression for applications like wireless communications and mobile devices. The key products and services to be delivered include: 1) Investigating...
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...
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 $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 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...
This three-year, $276,611 National Science Foundation Project Grant supports research at the University at Albany to develop algorithms and theory for compressing deep neural networks. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), this award will advance knowledge in discrete optimization and machine learning. Key products include new coarse gradient and thresholding algorithms to enable efficient deployment of AI systems on mobile and low-power platforms. By...
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...