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 award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $596,797 to the University of California, San Diego (UCSD) to develop methods for creating interpretable and robust deep neural network models. The key products and services to be delivered through this 3-year award (9/1/2024 - 8/31/2027) include: Automating the interpretation of deep neural networks using human-understandable concepts without...
This 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 novel approach to enhancing the transparency, reliability, and efficiency of artificial intelligence (AI) systems. The $150,000 award, made effective May 1, 2025 with an ultimate completion date of April 30, 2027, will enable researchers at the University of North...
This award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), funds a collaborative research project led by Northeastern University to develop new methodologies for visualizing, understanding, and rewriting the learned computations of multimodal generative artificial intelligence (AI) models. The $400,000 project, awarded on September 15, 2024, seeks to address the unpredictability and potential safety...
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 award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop Algorithm-Informed Neural Networks (AINNs) - a new approach that integrates well-established algorithmic principles into the design of neural networks. The $150,000 award, effective May 1, 2025 through April 30, 2027, will enable the University of Missouri at Kansas City (UMKC) to enhance the explainability, reliability,...
This Project Grant award of $680,733 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research towards developing methods for interpreting deep learning models. The University of Houston System is the prime awardee for this 1.7-year project, which aims to improve the usability and trust in deep learning systems for real-world applications like healthcare and cybersecurity. The research explores post-hoc...
This $400,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the University of California, Berkeley to develop new methodologies for understanding, visualizing, and controlling the behavior of complex, large-scale AI models that generate both text and images. The project aims to create a systematic framework for tracing how the training data used influences the internal...
This $395,927 Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) aims to develop theoretical and algorithmic foundations for building a safe and sustainable human-AI ecosystem. The key objectives are: Developing an analytical framework to characterize human-AI interactions and embed safety considerations for both the AI learner and human agents. Examining the feedback effects between human and AI agents, and developing...
The National Science Foundation (NSF) awarded a $111,878 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to New York University (NYU) for the project "CAREER: A Hybrid Parametric and Nonparametric Approach for Grounding Visual Intelligence in the Real World." The project aims to develop a hybrid framework that integrates intuitive and deliberate visual processing methods to create more robust artificial intelligence (AI) systems capable of...