Project Grant 2544246
- Federal Grant Award Summary The University of Texas at Austin received a $195,605 Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective July 1, 2025, through June 30, 2030. This CAREER award funds research to develop innovative frameworks for distributed data compression and communication algorithms that integrate information theory, coding theory,...
- Federal Grant Award Summary The University of Texas at Arlington received a $408,211 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070), effective July 1, 2026 through June 30, 2031. This CAREER award supports the development of an artificial intelligence (AI)-driven integrated sensing, computing, and communication (ISC2) framework designed to enable...
- Federal Project Grant Award Summary The University of Texas at Arlington received a $120,000 Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), awarded October 1, 2025, with completion targeted for September 30, 2027. This collaborative research initiative addresses intellectual property protection challenges posed by generative artificial intelligence...
- Federal Grant Award Summary The National Science Foundation (NSF) awarded the University of Houston System $179,988 under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) on August 1, 2025, for a collaborative research project titled "SGCC: An Efficient GPU-Oriented Data Reduction Cyberinfrastructure for Scientific Data Analysis." The three-year project, concluding July 31, 2028, develops the Scientific GPU Compression Cyberinfrastructure (SGCC)—a...
- Federal Grant Award Summary The University of Texas at Arlington received a $321,890 CAREER Award from the National Science Foundation's Division of Computing and Communication Foundations (CFDA 47.070 - Computer and Information Science and Engineering) beginning June 1, 2026, and concluding May 31, 2031. This project grant supports the development of interactive sensor-driven tools and a sensing platform designed to enable observation, measurement, and documentation of material behavior...
- Federal Grant Award Summary The National Science Foundation's Division of Information and Intelligent Systems awarded Stevens Institute of Technology a $171,387 Computer and Information Science and Engineering (CFDA 47.070) Project Grant effective August 1, 2025, through July 31, 2027. This research initiative develops a compression-aware computing framework to enhance the efficiency of machine learning (ML) model inference on resource-constrained devices. The project addresses the fundamental...
- 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...
- Federal Grant Award Summary The National Science Foundation's Division of Electrical, Communications and Cyber Systems awarded the University of Texas at Dallas a CAREER grant valued at $501,234 on May 1, 2026, under the Engineering program (CFDA 47.041). This five-year project, concluding April 30, 2031, supports fundamental research in amorphous oxide semiconductor (AOS) nanoelectronics for three-dimensional (3D) integrated computing systems. The primary deliverables include scientific...
- Federal Project Grant Award Summary The University of Texas at Dallas received a $365,846 CAREER grant from the National Science Foundation's Division of Computer and Network Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award, effective October 1, 2025 through September 30, 2030, funds cross-layer optimization research to advance deoxyribonucleic acid (DNA) storage systems as a viable long-term archival solution for preserving digital data....
- Federal Grant Award Summary The University of Texas at Austin received a $333,000 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems (CISE Program, CFDA 47.070) awarded October 1, 2025, with completion targeted for September 30, 2028. This collaborative research initiative develops a novel neurosymbolic programming framework called Foundation Model Programming designed to generate symbolically interpretable scientific hypotheses from...
The University of Texas at Arlington received a $347,035 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070), effective June 1, 2026 through May 31, 2031. This CAREER award supports the development of an algorithm-hardware co-design framework for high-performance scientific data compression that integrates artificial intelligence (AI) and neural learning methods with classical compression techniques. The project addresses the computational gap between intensive data compression workloads and insufficient hardware support in high-performance computing (HPC) systems that process massive volumes of simulation data from scientific domains including climate change, astrophysics, fluid dynamics, and biological research. The research comprises three integrated components: establishing algorithmic principles for multi-mode neural compression that enhances compression quality and reliability for diverse scientific data; designing specialized hardware architectures optimized for the distinctive computation and dataflow patterns of AI-fused neural compression; and developing implementation strategies to enable practical deployment across HPC infrastructure. Upon completion, this research will improve the usability of large-scale scientific simulations and strengthen computing infrastructure supporting research across multiple scientific disciplines by significantly enhancing the efficiency of data storage, transfer, and input/output performance in HPC systems.Federal Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $347.0k | 5/7/26 |