Project Grant 2210388
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides USD 125,000 in funding to the University of California, Davis to advance the field of Generalized Fiducial Inference (GFI). The key objectives are to extend GFI methods to causal inference models, particularly instrumental variable models, and redefine GFI through normalizing flows to manage computational complexity in non-analytic scenarios. The project...
- 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 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 $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 $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 $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...
- 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...
- 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...
- 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 $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...
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 algorithms for sampling from generalized fiducial distributions, which will improve the practical applicability of generalized fiducial inference and enable further theoretical study. Researchers will also investigate fundamental issues of applying generalized fiducial inference on manifolds and lay the groundwork for non-parametric problems. As an important application, the project will develop post-hoc calibration methods for measuring strength of evidence in forensic science. Graduate students, including underrepresented groups, will gain research experience through involvement in the project. The award is expected to expand the understanding of statistical foundations and have applications in forensics, genomics, differential privacy, and spatial statistics.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $170.0k | 6/14/22 |