Project Grant 2538206
Award Date
10/1/25
Completion Date
9/30/27
Dollars Obligated
$200K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Charlottesville, VA, USA
Similar Awards
This $1,000,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports the development of generative imaging models to verify and explain machine learning systems for healthcare applications. The key goals include: Developing robust "robustness audits" using synthetic data to assess how well a healthcare deep learning system will operate at different clinical sites, given variability in...
This Project Grant award of $499,997.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a new framework to improve the interpretability and steerability of domain-specific AI models in medical imaging. The key products and services to be delivered include: Constructing an anatomically aware vision-language model capable of encoding and generating 3D medical images and radiology reports, to reduce the risk of...
This four-year, $622,992 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop methods for making machine learning models more interpretable and reliable. Specifically, researchers at the University of Virginia will investigate the mathematical foundations of deep neural networks, with a focus on geometry and topology, to better understand internal representations. Computational tools will be designed based on these...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to improve the efficiency and reliability of explainable AI (XAI) systems. The project aims to accelerate computationally intensive XAI algorithms, develop unified explainer models, and validate the methods in medical applications like histopathology imaging and cancer prognosis. This work will establish a...
This Project Grant award for $600,000, provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research at Brown University to develop new computational methods for integrating heterogeneous health data. The goal is to improve predictive modeling and explainable artificial intelligence techniques for advancing personalized healthcare and treatment. The key products of this 5-year project include novel graph-based...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 to The Pennsylvania State University from October 1, 2022 to September 30, 2025. The university will develop interpretable machine learning methods based on deep neural networks from a source coding perspective. Researchers will draw an analogy between explaining complex prediction models and transmitting signals with limited channel capacity. The...
This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $220,000 to Yale University from September 1, 2023 through August 31, 2027. The project aims to develop a smarter artificial intelligence (AI) system to better understand and analyze complex medical images, such as those from multiple scans of a patient. The research team will tackle challenges to make the AI system more scalable, interpretable,...
This Project Grant award of $348,227 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at the University of Delaware to improve the scalability and effectiveness of large language models (LLMs) for healthcare applications. The key objectives are to develop methods for evaluating LLM performance and mitigating issues with incomplete data, ensuring truthful and transparent LLM outputs. The research also integrates...
This $150,000 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the development of Algorithm-Informed Neural Networks (AINNs) - a new approach that integrates well-established algorithmic principles into the design of neural networks. The goal is to enhance the explainability, reliability, and efficiency of AI systems, making them more transparent and reducing their dependency on large...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program, with a total funding amount of $584,034, supports research to improve the interpretability of machine learning models in computer vision. The project aims to develop new technologies that allow machine learning-based computer vision models to explain their reasoning processes to human users and enable users to interact with the models to correct mistakes. Key...