Cooperative Agreement N660011724028

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 Project Grant award from the National Science Foundation's STEM Education (CFDA 47.076) program provides $450,000 to Princeton University to develop "Differentiable Logic Networks" - an interpretable and energy-efficient approach to implementing artificial intelligence (AI) and machine learning frameworks. The project aims to address the key challenge of making AI-based decisions more transparent and explainable, particularly for applications in domains like medical and legal...

The National Science Foundation (NSF) awarded a $582,031 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the Regents of the University of Michigan, doing business as the University of Michigan. The grant will fund a 5-year research project focused on "Achieving Explainable Artificial Intelligence (AI) Through Human-AI Interaction." The goal of the project is to develop new scientific knowledge and design guidelines for delivering...

This Project Grant from the National Science Foundation's $524,474 Computer and Information Science and Engineering program will support the development of artificial intelligence systems capable of understanding general real-world tasks and providing step-by-step visual and language guidance to solve complex problems. Over a three-year period from June 2022 to May 2025, researchers at the University of Minnesota will create a new dataset annotating diverse everyday tasks and solutions,...

This $600,000 project grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to advance artificial intelligence (AI) capabilities by investigating the mathematical foundations and practical applications of deep learning models. The project, awarded to the University of Wisconsin System's University of Wisconsin-Madison campus, will focus on understanding the properties of neural networks trained on...

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 Project Grant award from the National Institutes of Health (NIH) Office of the Director under CFDA 93.310 Trans-NIH Research Support program provides $150,000.00 to Emory University to develop a computational framework that leverages AI visual explanation capabilities to improve early diagnosis of abdominal cancers. The project aims to: 1) Enhance AI sample efficiency through visual explanation supervision and cancer imaging annotations, 2) Consolidate AI knowledge across...

This five-year $200,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a theoretical framework for analyzing deep neural networks using convex optimization techniques. The grantee, Stanford University, will conduct research to demystify deep learning models and improve their reliability, interpretability, and trustworthiness for artificial intelligence applications. Specifically, the project seeks to apply signal...

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 $1.6 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Pennsylvania from October 2022 through September 2026. The research focuses on developing theoretical tools to build an understanding of why deep neural networks (DNNs) work and when they can fail. Investigators will seek to identify common themes in how artificial and biological systems like the human brain learn. They will...