Project Grant 2451436

Award Date 8/1/25
Completion Date 7/31/27
Dollars Obligated $175K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Williamsburg, VA, USA
Similar Awards
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $175,000 to the University of California, Merced to advance trustworthy artificial intelligence (AI) systems. The project develops novel computational methods that integrate large language models with structured knowledge graphs, enabling AI systems to provide more reliable and factually accurate responses, particularly in high-stakes domains like...
The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund a $174,965 project at the College of William & Mary in Williamsburg, Virginia. The project, titled "CRII: III: REINFORCEMENT LEARNING FOR COMBINATORIAL OPTIMIZATION IN SOCIETAL PROBLEMS," aims to develop innovative artificial intelligence (AI) technologies to efficiently solve complex real-world combinatorial optimization...
This Project Grant award of $600,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at the University of Georgia Research Foundation to develop novel memory hierarchy optimizations for running transformer-based artificial intelligence (AI) models on mobile devices. The key objectives of the project are to: 1) create detailed performance models for executing transformer workloads on mobile GPUs, 2)...
This $400,000 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models. The key objectives are to: 1) create new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2) explore model...
This Project Grant award for $346,803 was provided by the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program. The grant supports research at the University of Chicago to investigate the mechanisms that determine the memorability of stimuli, such as images, voices, and words. The research will test three main theories about what makes certain information more memorable than others, using a combination of behavioral experiments, brain...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $400,000.00 to the University of California, Berkeley (UC Berkeley) to conduct research on understanding, visualizing, and attributing multimodal generative models - large-scale AI models that generate both text and images. The research aims to develop new systematic frameworks for analyzing the internal mechanisms and...
The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of South Carolina. The grant, with a period of performance from October 1, 2024 to September 30, 2027, focuses on enhancing security and mitigating harm in AI-generated vision language models. Key technical objectives include: 1) Developing a prompting framework for detecting harmful content provenance in AI-generated vision...
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...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant (CFDA 47.070) award of $175,331 will support the development and evaluation of novel AI-based methods for student modeling to trace students' competencies in computer science education. The project aims to tailor large language models (LLMs) to account for students' knowledge state, learning practices, and interactions with intelligent learning systems in order to generate more...

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will provide $174,983 to the College of William & Mary from August 1, 2025 to July 31, 2027 to develop new techniques for measuring and controlling memorization in text-attributed graph (TAG) models used in AI applications.

The key objectives are to: 1) introduce a dynamic prompting strategy to more precisely assess memorization rates, 2) create a dynamic pruning framework for fine-tuning memorization levels to optimize for either recall or privacy, and 3) establish a benchmark for evaluating how graph topology influences memorization in TAG models. This research aims to strengthen the reliability, privacy, and interpretability of AI models used in critical applications like healthcare, cybersecurity, and knowledge discovery. The findings will be shared through open-source tools, benchmark datasets, and academic collaborations to benefit the broader AI research community.

Generated 7/1/25, 2:28 AM