Project Grant 2515902
- This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) supports the development of novel Bayesian thresholding and shrinkage methods for extracting meaningful information from large, noisy datasets across diverse scientific and engineering domains. The $100,123 award to Texas A&M University, which runs from Sep 1, 2025 to Aug 31, 2028, focuses on advancing statistical techniques for signal recovery and noise reduction in multiscale...
- 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 $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
- 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 $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
- The National Science Foundation (NSF) awarded a $249,989 project grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of New Hampshire's (UNH) Office of Sponsored Research. The grant supports the development of efficient and robust statistical tools for modeling data with measurement errors, a common challenge in fields like epidemiology and economics. The research aims to improve estimation accuracy and hypothesis testing power across linear, nonlinear, and...
- The National Science Foundation awarded a $169,977 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "ROBUST AND EFFICIENT STATISTICAL INFERENCE IN LARGE SCALE SEMI-SUPERVISED SETTINGS." The three-year award, which runs from August 1, 2021 through July 31, 2024, will fund the development of statistical methods to enable robust and efficient inference on large, semi-supervised datasets. As the prime...
- The National Science Foundation (NSF) awarded a $270,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Virginia Polytechnic Institute & State University (Virginia Tech) to develop new statistical methods that incorporate heavy-tailed prior distributions. The overarching goals of the project are to: Unify feature extraction techniques in independent component analysis using novel latent space representations. Improve astronomical distance estimation by...
- This $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
- This $125,000 Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program will support research at the University of Texas at Austin to develop a Bayesian inference framework for learning earthquake cycle deformation processes across scales. The project aims to create an advanced framework capable of assimilating multi-modal observational data into high-resolution forward models to infer...
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 social sciences. Key technical elements include covariate-informed density deconvolution techniques and models for median-centered measurement errors. This work is expected to promote more accurate, data-driven decision-making in applied domains. The project also contributes to workforce development through graduate student training on modern data science approaches. The University of Texas at Austin, a leading research institution with extensive federal grant and contract experience, is the awardee for this 3-year project running from August 2025 to July 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 8/14/25 |