Project Grant 2431504
Award Date
1/1/25
Completion Date
12/31/27
Dollars Obligated
$145K
Funding Federal Agency
Division of Information and Intelligent Systems
Awarding Federal Agency
Division of Computer and Network Systems
Awardee
Yale Univ (FL6GV84CKN57)
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Hamden, CT 06511, USA
Similar Awards
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award under CFDA 47.070, totaling $246,872, aims to develop a time series text-based cross-modality question answering (QA) system. The project will address key challenges in leveraging large language models for time series data, including the lack of text information aligned with time series data, the need for deep learning models to reason across time series and text, and the lack of...
The National Science Foundation (NSF) has awarded a $260,000 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to The University of Texas Rio Grande Valley (UTRGV). This 3-year grant, effective from September 1, 2024 to August 31, 2027, aims to develop robust and human-aligned deep learning techniques for medical sensor time-series data analysis. The project will focus on: 1) identifying input confounders in time-series data at various levels, 2)...
This $174,878 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Texas Rio Grande Valley (UTRGV). The project aims to develop innovative algorithms to improve the effectiveness of contrastive self-supervised learning (CSSL) frameworks for time series data analysis. Specifically, the award will fund research to integrate time series motif detection into CSSL methods, addressing...
This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $549,999 to the University of North Texas to develop a time-sensitive large model training platform for dynamic data analytics. The primary objectives are to: Automatically generate a parallelization plan to minimize training iteration latency for large-scale deep learning models. Progressively grow models from pre-trained small models...
This $200,000 Project Grant from the National Science Foundation's STEM Education program (CFDA 47.076) will fund two key initiatives at Texas A&M University-San Antonio, a Hispanic-Serving Institution: A "Foundational and Use-Inspired AI Research Initiative" focused on improving healthcare quality of life, transforming national defense and security, and enhancing cloud computing capabilities. The expected outcomes include novel contributions to medical imaging reliability, reduced...
This Project Grant award from the National Science Foundation (NSF) under the Integrative Activities program (CFDA 47.083) provides $286,612 to the University of Nevada, Las Vegas (UNLV) to develop an explainable AI-supported performance monitoring system for distributed sustainable energy networks. The project aims to improve the reliability of sustainable energy systems by detecting and classifying anomalies using multi-modal learning and explainable AI techniques. The research will contribute...
This $250,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program will support the development of generative artificial intelligence (AI) frameworks to aid scientific reasoning and accelerate sustainable development. The central goal is to create generative AI systems that can efficiently analyze vast scientific data to identify patterns, simulate natural phenomena, and suggest...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $680,733 to the University of Houston System to conduct research on improving the interpretability of deep learning models. The project aims to develop new algorithms and methods to shed light on how deep learning models make predictions, generate representations, and incorporate interpretability directly into the model structure. The...
This $300,000 EAGER (Early-concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a framework called XAISE (eXplainable Artificial Intelligence for Science and Engineering) to enhance the explainability of artificial intelligence (AI) models for scientific and engineering applications. The project seeks to design, develop, and implement XAISE to improve the explainability of...
This $300,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) on April 15, 2025. The grant will fund a collaborative effort led by Northern Kentucky University, in partnership with the Computing Research Association, Texas State University, and Kennesaw State University, to engage faculty at research-emerging institutions with large computing programs. The primary goals of the project...