Project Grant 2507523
- 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 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...
- This is a $342,235 Project Grant awarded on October 1, 2025 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The grant was awarded to the University of California, Irvine to develop novel explainability tools and techniques that enable effective human-AI collaboration during the design and deployment of learning-based network controllers. Key objectives include developing a concept-based explainer to...
- 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 two-year, $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop techniques for improving the interpretability and robustness of deep neural networks. Specifically, the University of California, Santa Barbara will apply ideas from communication theory and neuroscience to actively shape the features extracted by individual layers of neural networks in addition to end-to-end training. By learning "matched...
- This Project Grant award of $175,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program advances trustworthy artificial intelligence (AI) by developing methods to integrate large language models with structured knowledge graphs. The research, conducted by the University of California, Merced, aims to create more reliable and accurate AI-powered question answering systems. The key technical advances include synergistic knowledge...
- 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...
- This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program aims to develop scalable algorithms and methods for improving the explainability and robustness of Artificial Intelligence (AI) models. The $350,000 grant awarded to the University of California, Berkeley will leverage spectral analysis and coding theory techniques to identify key input features and interactions that drive AI model predictions. This research seeks to enhance transparency and trustworthiness of AI systems, particularly for critical applications in areas like healthcare and national defense. The 3-year project will also investigate the influence of training data on model behavior and explore data acquisition strategies to address potential imbalances. Findings from this work have the potential to accelerate scientific discovery in domains such as drug design and protein engineering through improved understanding of complex AI-powered analysis.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $350.0k | 7/20/25 |