Project Grant FA23862514062

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...

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 $167,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a comprehensive framework for making Graph Neural Network (GNN) predictions explainable and trustworthy. The research project, led by The Pennsylvania State University, aims to address the critical need for artificial intelligence systems that can provide accurate predictions while also explaining their reasoning in a way...

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...

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 two-year Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund research at the University of Southern California to develop more explainable artificial intelligence agents with common sense capabilities. The grant aims to advance techniques for AI agents to reason in novel situations, complete open-world narratives, and provide commonsense...

This $150,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of foundational principles, algorithms, and tools for causal decision-making systems. Researchers at Columbia University will enrich traditional artificial intelligence formalism with causal modeling to enable more efficient, robust, and explainable decision-making by autonomous systems. Key deliverables include integrating...

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) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $592,294 to The Pennsylvania State University to develop secure, robust, and end-user driven prediction-aware counterfactual explanations (CFEs) for machine learning (ML) models. The 3-year project aims to address key limitations of current CFE techniques, including the potential for intellectual property theft, inability to handle model updates, and lack of...

This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $576,351 to Purdue University to advance fair and explainable artificial intelligence through new algorithms and human-centered approaches. The award supports research activities from August 15, 2022 to July 31, 2027 to develop a family of fair, explainable, and robust data mining algorithms with...