Project Grant 2515767
- This Project Grant award of $159,940 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to Rutgers, The State University will develop a next-generation statistical framework called Predictability-Computability-Stability Inference (PCSI) to improve the reliability and reproducibility of data science and artificial intelligence. The research aims to ensure conclusions drawn from data are accurate, stable, interpretable, and computationally practical,...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This Project Grant award of $148,654 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop statistical tools to improve the reliability of artificial intelligence (AI) used in real-world applications such as automated decision-making, financial forecasting, and neuroscience research. The research will establish mathematically rigorous methods for uncertainty quantification to build trustworthy AI, with applications including enhancing...
- This $599,411 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a "Trustworthy Toolbox for Double-Correct Predictive Modeling in Sciences." The project aims to create advanced artificial intelligence (AI) and machine learning (ML) models that can make accurate predictions while also providing transparent, scientifically-grounded rationales for their outputs. This...
- This $175,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of a unified generative prediction and inference framework using diffusion processes, normalizing flows, and transfer learning to model joint distributions of tabular and unstructured data. The key products and services delivered under this award include: Algorithms for domain adaptation, reliability metrics for trustworthy AI,...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
- This federal Project Grant award of $100,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports research on advanced probabilistic models and their application to cutting-edge machine learning techniques. The research aims to bring mathematical rigor and develop new methods related to complex systems in areas such as image processing, reinforcement learning, and generative AI. Key focus areas include: 1) extracting...
- This $245,190 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance the integration of modern machine learning tools, such as deep learning and Bayesian additive regression trees, into statistical modeling frameworks. The research program has two key objectives: Developing a novel Bayesian inferential framework for "generative models" - statistical models where data is viewed as stochastic outputs of...
- This federal Project Grant award of $180,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Michigan State University to improve the robustness and trustworthiness of artificial intelligence (AI) models. The project aims to establish statistical frameworks for adversarial training in neural networks and develop scalable algorithms that leverage dynamic attack strategies and selective sampling to enhance the...
- This $250,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by the University of Washington to explore the use of machine learning and artificial intelligence algorithms to augment limited datasets and improve statistical inference. The project will take a three-pronged approach: 1) establishing new semi-parametric efficiency results for semi-supervised learning, 2) developing new and improved...
This $140,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI) methods. The project, titled "Collaborative Research: Performance Guaranteed Statistical Learning with Multiple Classes of Models (Guided by PCS)," aims to advance a framework called Predictability-Computability-Stability Inference (PCSI) for veridical data science. This research will help ensure data-driven conclusions are accurate, stable, interpretable, and computationally practical, strengthening scientific integrity and public trust in AI and data-driven decisions. The award, which runs from August 2025 through July 2028, will also train the next generation of data scientists and promote interdisciplinary collaboration.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $140.0k | 8/7/25 |