Project Grant 2513768
Award Date
7/1/25
Completion Date
6/30/28
Dollars Obligated
$300K
Federal Agency
Office of Advanced Cyberinfrastructure
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Chicago, IL 60637, USA
Similar Awards
This Project Grant award of $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund the development of a cyberinfrastructure to seamlessly integrate lossy data compression into deep learning pipelines for scientific applications. The key products and services to be delivered include: A user-friendly interface allowing users to define accuracy requirements and instantiate different data compression algorithms...
This Project Grant award, valued at $299,999.00, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The project aims to develop a novel learning-driven framework to mitigate artifacts produced by scientific data compressors, which can distort both raw and post-hoc data analytics. The research will focus on characterizing compression artifacts, designing deep learning models to tackle artifact mitigation, and...
This Project Grant award of $299,354 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel learning-driven framework to mitigate artifacts produced by scientific data compressors. The goal is to improve the integrity and quality of lossy-compressed scientific data, facilitating efficient data storage, transmission, and analytics across domains such as climatology, cosmology, fusion energy...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $171,387 to The Trustees of the Stevens Institute of Technology to advance the efficiency of machine learning model inference through a compression-aware computing framework. The key objectives are to: 1) Develop machine learning models capable of self-awareness in response to lossy compression, enabling them to detect, identify, and...
The National Science Foundation (NSF) awarded a $175,000 Computer and Information Science and Engineering (CISE) Program grant to The Trustees of the Stevens Institute of Technology to develop a compressor-assisted collective communication framework for large-scale deep learning on GPU-based systems. The two-year project aims to address challenges with the communication overhead of training massive deep learning models by investigating efficient lossy compression techniques for gradient data and...
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 $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 $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 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...