Project Grant 2403075
- This $499,999 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support collaborative research to investigate the theoretical foundations of compositional learning in large language models (LLMs) based on transformer architectures. The research aims to advance the understanding of how LLMs, such as GPT-4, LLAMA 2, and CLAUDE 3, can decompose complex tasks into simpler intermediate steps to...
- This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to advance artificial intelligence (AI) by investigating the mathematical foundations and practical applications of deep learning models. The $600,000 award, with a performance period from December 2024 to November 2027, will support research focused on understanding the properties of neural networks, the function spaces and data representations that emerge...
- This $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The objective of the award is to leverage and evaluate large language models (LLMs) as "tools for thought" that can support creative, open-ended, and collaborative work across various applications such as scientific writing, text analysis, and design ideation. The key products and services to be developed and...
- This $400,000 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models. The key objectives are to: 1) create new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2) explore model...
- Federal Project Grant Award Summary Michigan State University received a $268,000 Project Grant awarded October 1, 2025, through the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070), Division of Information and Intelligent Systems. This collaborative research project, titled "Advancing Large Language Model Unlearning: Foundations and Applications," will develop foundational research and algorithmic frameworks to enable the...
- This $400,000 Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The project aims to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models, in order to increase the accountable and safe use of these advanced AI systems and mitigate potential harms. The key research thrusts involve: 1) new...
- The National Science Foundation awarded a $400,000 Project Grant to Stanford University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will support research and education activities to develop conceptual and mathematical understanding of large language models. Specifically, the university will conduct research analyzing simplified generative text models and language models trained on such data to gain insights into their inner...
- This Project Grant award, valued at $600,000.00, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The award, with a performance period from January 1, 2026 to December 31, 2028, is supporting research by the Regents of the University of Michigan in the areas of extremal combinatorics and the analysis of algorithms. The primary goals are to solve basic classification questions in the theory of...
- This Project Grant award of $600,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at the University of Georgia Research Foundation to develop novel memory hierarchy optimizations for running transformer-based artificial intelligence (AI) models on mobile devices. The key objectives of the project are to: 1) create detailed performance models for executing transformer workloads on mobile GPUs, 2)...
This $400,000 Project Grant was awarded on July 1, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to the Regents of the University of Michigan to conduct collaborative research on the theoretical foundations of compositional learning in large language models based on transformer architectures. The research aims to investigate three key areas: model expressivity, statistical learning theory, and optimization, with the goal of developing novel learning guarantees, algorithms, architectures, and design principles to advance the capabilities and interpretability of artificial intelligence and large language model systems. The findings will be incorporated into educational curricula to foster a diverse community around transformers and compositional learning, and outreach activities will promote responsible AI practices and AI education for undergraduate and K-12 students. No subawards are planned for this award.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 4/16/24 |