Project Grant 2515304
Award Date 9/1/25
Completion Date 8/31/28
Dollars Obligated $125K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Davis, CA, USA
Similar Awards
- This Project Grant award, provided by the National Science Foundation under the Mathematical and Physical Sciences (CFDA 47.049) program, supports collaborative research to advance Generalized Fiducial Inference (GFI) methods. The $125,000 award to the University of North Carolina at Chapel Hill aims to extend GFI techniques for causal inference models and redefine GFI through normalizing flows to address computational complexities in non-analytic scenarios. The research will apply these...
- This National Science Foundation Project Grant award of $340,000 provides funding from September 1, 2022 through August 31, 2025 to support collaborative research on emerging variants of generalized fiducial inference. The award is made under the Mathematical and Physical Sciences program (CFDA 47.049) to further the Foundation's mission of advancing the mathematical and physical sciences. Specifically, the University of California, Davis will conduct research to develop easy-to-implement...
- This three-year Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), provides $320,000 to the University of North Carolina at Chapel Hill to conduct collaborative research on emerging variants of generalized fiducial inference. The research aims to explore the evolution of the fiducial argument as a response to modern data science questions and techniques. Researchers will develop...
- 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 $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
- This $270,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The grant supports research focused on tightening the connections between geometric and topological data analysis and statistics/machine learning. Key objectives include developing advanced theoretical tools and methodologies based on core concepts like the Euler characteristic, graph Laplacians, and heat kernel, as well as training...
- This $155,000 project grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) will develop new simulation-based inference (SBI) methods. These innovations aim to empower scientists to make better use of complex models across diverse domains such as genetics, ecology, biology, economics, and psychology, supporting more scalable, efficient, and reliable decision-making. The project will address two core challenges...
- This federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $140,000.00 to develop a rigorous theoretical foundation for amortized inference, a novel paradigm in machine learning, statistics, and simulation. The research project aims to (1) deepen the understanding of the mathematical principles underlying amortized inference and (2) design improved methods with provable guarantees. Key objectives include...
- The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the University of California, Davis. This 3-year award from July 1, 2025 to June 30, 2028 will support research on nonlinear functional time series analysis. The project aims to develop statistical methods and forecasting algorithms for analyzing complex data observed as functions or curves, such as yield curves used in economics. Key outcomes...
- The National Science Foundation awarded The Regents of the University of California $330,000 under the Mathematical and Physical Sciences program (CFDA 47.049) to advance theory and methodology for tree-based algorithms in high dimensions from July 2022 to June 2025. The project will analyze the generalization performance of tree-based algorithms on regression models to better understand their inductive bias for different data structures. It will also study a new framework for obtaining...