Project Grant 2451398

Award Date 8/1/25
Completion Date 7/31/27
Dollars Obligated $171K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Hoboken, NJ 07030, USA
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This Project Grant award of $299,999 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 goal is to enable scalable, AI-driven scientific discovery by reducing memory usage and communication overhead for AI-for-science applications that utilize massive datasets. Key...
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...
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 $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 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...
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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...
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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 localize compression; and 2) Leverage this self-awareness to recover and enhance model performance without retraining, using techniques like instruction-based recovery and sparse zeroth-order optimization. The project will evaluate these methods on real-world tasks like language modeling and question answering. By addressing limitations in compressed machine learning inference, this award aims to contribute practical tools for efficient model deployment on consumer devices.

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