Project Grant 2210206
- This National Science Foundation Project Grant award of $359,976 provides funding from June 15, 2022 through May 31, 2025 to develop new statistical methods for scalable inference in high-dimensional structured regressions. The awardee is Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049). Specifically, the researchers will develop approaches based on compressing large datasets using random linear transformations prior to fitting statistical models....
- This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...
- This Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...
- The National Science Foundation awarded $150,000 to Regents of the University of California at Riverside under the Mathematical and Physical Sciences program (CFDA 47.049) from July 1, 2023 to June 30, 2026. The Project Grant funding will support research to develop new statistical methodologies and deep learning techniques for uniformly estimating causal effects of continuous treatments using large observational health data sets. Specifically, the university will design neural network...
- The University of California, San Diego (UCSD) received a $300,000 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The grant supports the development of advanced computational and statistical methods for analyzing complex, dependent data from diverse scientific domains such as climate, economics, and neuroimaging. Key research thrusts include subsampling and resampling techniques for large datasets, robust...
- This National Science Foundation project grant of $239,962 will support the development of novel statistical methods for analyzing functional and imaging data supported on complex geometries through the Mathematical and Physical Sciences program (CFDA 47.049). Led by the University of Washington with a completion date of June 2025, key products include generalized linear models and regularized linear models to predict outcomes from functional predictors on multidimensional non-linear domains....
- 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...
- Federal Project Grant Award Summary The University of California, Davis received a $175,000 project grant from the National Science Foundation's Division of Mathematical Sciences (CFDA 47.049 – Mathematical and Physical Sciences) effective July 1, 2025 through June 30, 2028. This research initiative develops advanced statistical and computational methodologies to enhance the reliability of data analysis for high-dimensional, noisy datasets with complex temporal and nonlinear structures. The...
- 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 $600,000 project grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, will support research to develop a new Bayesian diffusion model framework for advanced visual perception and cognition systems. The University of California, San Diego (UCSD) will serve as the primary awardee, with the goal of revisiting the analysis-by-synthesis methodology by integrating generative priors into the learning and inference...
This National Science Foundation Project Grant of $220,000 supports research into statistical modeling methods for large, complex datasets. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of California, San Francisco will develop new Bayesian regression techniques using random data compression matrices. These approaches aim to enable efficient, scalable inference and prediction from high-dimensional biomedical data sources like brain imaging, genetics, and electronic health records. By compressing large datasets, the methods seek to facilitate storage-efficient, theoretically optimal modeling with rich parametric and nonparametric models. The researchers will validate the approaches on neurological disorder studies combining UK Biobank data. Outcomes include software toolkits and training opportunities advancing interdisciplinary statistical sciences research. Completion is slated for May 2025.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $110.0k | 6/14/22 |