Project Grant 2515275
Award Date 8/15/25
Completion Date 7/31/28
Dollars Obligated $175K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Irvine, CA 92697, USA
Similar Awards
- 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,...
- This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $250,000 to The Trustees of the University of Pennsylvania to develop advanced statistical methods for integrating and analyzing large-scale data from multiple sources, such as electronic health records and genomics data. The project aims to devise new data-driven algorithms with theoretical optimality guarantees for transfer learning, as well as adversarially...
- 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...
- 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 $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...
- This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
- The National Science Foundation (NSF) awarded a $155,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Trustees of Columbia University in the City of New York. The grant, awarded on August 1, 2025 with a completion date of July 31, 2028, will develop powerful new methods for integrating diverse and evolving datasets - a critical challenge in modern science and technology. The research will create flexible and reliable tools to automatically...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program aims to address theoretical challenges in high-dimensional probability, with a focus on applications in data science. The $239,966 award, made on July 15, 2025, will support research to develop rigorous mathematical frameworks for understanding the authenticity and privacy of synthetic data, as well as advancing non-spectral random matrix theory....
- This $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of novel Bayesian statistical frameworks to address measurement error challenges in complex multivariate data. The project aims to create more flexible, data-driven methods that can reliably identify meaningful patterns and relationships from noisy, imprecise observations - a common issue in fields like health research, astronomy, and...