Project Grant 2443633
Award Date
7/1/25
Completion Date
6/30/30
Dollars Obligated
$550K
Federal Agency
Office of Advanced Cyberinfrastructure
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Denton, TX 76205, USA
Similar Awards
This Project Grant from the National Science Foundation's $201,262 Computer and Information Science and Engineering program (CFDA 47.070) supports the development of interactive training materials and workshops on deep learning systems and applications in advanced GPU cyberinfrastructure. The University of North Texas will lead the effort in collaboration with Southern Illinois University Carbondale from December 1, 2022 to November 30, 2024. Under the award, the University of North Texas will...
This $129,999 federal Project Grant award, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), supports the development of SPEED, a scalable and efficient modeling framework that enables automated, real-time decision-making for high-performance computing (HPC) systems. The project aims to capture the complex relationships between HPC configuration settings and application performance using generative artificial...
The University of North Texas received a three-year, $398,684 Project Grant award from the National Science Foundation under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support an Research Experience for Undergraduates Site focused on interdisciplinary research exploring accelerated deep learning approaches through hardware-software collaboration. The project aims to advance development and use of computing infrastructure to enable and accelerate...
This $600,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop efficient training methods for Dynamic Graph Neural Network (DGNN) models on large-scale, time-varying graphs. The project aims to create innovative approaches for graph partitioning, sampling, caching, and training to enable highly scalable and efficient DGNN execution on time-varying graphs, which are...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
The National Science Foundation (NSF) awarded a $174,200 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of North Carolina at Chapel Hill (UNC-CH) to develop new resource allocation policies for optimizing the scheduling of parallelizable machine learning (ML) training workloads on shared hardware clusters. The goal is to enable the rapid and efficient training of highly accurate ML models using limited computing resources. This...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
The National Science Foundation Office of Advanced Cyberinfrastructure awarded the University of Texas at Austin a $1.2 million Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) from September 1, 2022 to August 31, 2025. The grant funds research to develop a rigorous and reliable scientific deep learning framework for forward, inverse, and uncertainty quantification problems in computational science and engineering. Specific objectives include...
This $250,000 five-year Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of generative artificial intelligence (AI) frameworks to accelerate scientific discovery and modeling in key domains like climate and energy. The project aims to create AI systems that can efficiently learn from and analyze large-scale scientific data to identify patterns, simulate natural phenomena,...
This NSF CAREER project award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $599,707 to Rutgers, The State University to develop innovative algorithms, systems, and interface designs to enable efficient and scalable training of large foundational deep learning models on supercomputers. The research aims to address key challenges in the performance, scalability, and human effort required for large-scale...