Project Grant 2502560
- This $166,667 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support a collaborative research project to uncover the mathematical, statistical, and contextual mechanisms underlying memorization in artificial intelligence (AI) models. The research aims to develop foundational theory and insights that will enable the creation of more reliable, robust, and privacy-preserving AI systems, addressing critical...
- The National Science Foundation (NSF) awarded a $174,983 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program to the College of William & Mary. The grant, titled "CRII: III: DYNAMIC PROMPTING AND PRUNING FOR MEASURING AND CONTROLLING MEMORIZATION IN TEXT-ATTRIBUTED GRAPHS", will be performed from August 1, 2025 to July 31, 2027. The project aims to develop methods for measuring and controlling memorization in text-attributed...
- The National Science Foundation (NSF) awarded a $100,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to Purdue University. The award, with a performance period from September 1, 2025 to August 31, 2026, will support research on advanced probabilistic models with potential applications in cutting-edge machine learning techniques. The project aims to develop new methods related to complex systems in image processing, reinforcement learning, and...
- This $148,654 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports collaborative research to develop statistical tools for improving the reliability of artificial intelligence (AI) systems. The research aims to establish mathematically rigorous methods for uncertainty quantification to build trustworthy AI with applications in automated decision-making, financial forecasting, and neuroscience research. The work will focus on...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $240,000.00 to The Trustees of Columbia University in the City of New York to develop methods that enhance trust and control over machine learning (ML) and artificial intelligence (AI) technologies. The project aims to create computationally efficient algorithms that can approximate the output of an ML model trained without a given subset of...
- The National Science Foundation awarded a $600,000 Project Grant to the Trustees of Boston University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will support research into developing new differentially private stochastic optimization algorithms for training neural networks while preserving individual privacy. Specifically, the grantee will investigate fundamental tradeoffs between privacy and performance in modern...
- This $150,000 federal Project Grant awarded on May 1, 2025 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of "Algorithm-Informed Neural Networks (AINNs)," a new approach that integrates well-established algorithmic principles into neural network design. The goal is to enhance the explainability, reliability, and efficiency of artificial intelligence (AI) systems, making them more interpretable and...
- This $350,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program on August 1, 2025. The grant will be used by the University of California, Berkeley to develop scalable algorithms for making artificial intelligence (AI) model predictions more understandable and explainable. This research aims to advance the transparency and trustworthiness of AI systems, which is critical for their safe deployment in applications...
- This Project Grant award of $120,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to advance the mathematical foundations of generative artificial intelligence (AI) models, particularly diffusion models. The award enables researchers at the University of Missouri System to develop new theoretical tools to elucidate how flow-based generative models produce novel outputs and extend these models to...
- The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Purdue University. The grant supports the development of a new class of "gradient-based discrete Markov Chain Monte Carlo (GD-MCMC)" algorithms designed to improve the efficiency, scalability, and statistical reliability of machine learning systems that handle complex, discrete data. The research aims to advance trustworthy AI...
This federal Project Grant award for $333,333.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support collaborative research to uncover the mathematical, statistical, and contextual factors behind memorization in artificial intelligence (AI) models. The project aims to develop foundational theory that will elucidate the principles affecting memorization in AI, culminating in the development of more reliable, robust, and privacy-preserving AI models. The research framework combines optimization theory, dynamical systems theory, and information theory to provide insights into the interplay between memorization, generalization, privacy, reproducibility, and model robustness. The findings are expected to lead to the creation of AI models less susceptible to privacy violations, overfitting, and adversarial attacks, thereby enhancing the trustworthiness and applicability of AI across diverse domains. The award was granted to Purdue University, a prominent 1862 Land Grant College and educational institution with extensive expertise in federal research and development initiatives.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $333.3k | 7/21/25 |