Project Grant 2521573
- 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,...
- This Project Grant award of $299,994.00 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program is funding the development of a statistics-based framework to measure and mitigate geographic bias in geospatial AI (GeoAI) models and foundation models. The key products to be delivered include: A set of geographic bias metrics grounded in spatial point pattern analysis that can quantify bias across different geospatial tasks and models. A...
- This $150,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of new clustering techniques and software packages at San Jose State University. The awardee will create a family of versatile mixture models to analyze mixed-type data with asymmetry, outliers, and missing values. Novel statistical approaches and latent class models will allow the techniques to handle high-dimensional, continuous, discrete,...
- This Project Grant award, valued at $325,000.00, was provided by the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program to the University of Washington. The project develops powerful new tools for understanding complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. The research introduces novel approaches to analyze messy, heterogeneous, and large...
- 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 Division of Behavioral and Cognitive Sciences awarded Trustees of Boston University a $575,862 Project Grant under the Social, Behavioral, and Economic Sciences federal grant program (CFDA 47.075). The grant supports research to develop advanced statistical techniques and spatial machine learning methods for accurately predicting land values and conservation costs across large, heterogeneous geographic areas in the United States. The grantee will compare...
- This Project Grant award of $194,675 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to develop new econometric and statistical methods that allow researchers to draw valid conclusions from complex data in a variety of social, behavioral, and medical science settings. The key products and services to be delivered include: Developing a generally applicable reparameterization procedure for informative...
- This $139,660 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports research at the College of William and Mary to develop novel online data mining algorithms that can provide transparent and interpretable machine learning models for real-time applications such as crowd movement prediction, disaster monitoring, and pandemic response. Key objectives include: 1)...
- This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $359,940, will support the development of new statistical models and algorithms for analyzing large, spatially-dependent data sets collected from complex domains with irregular boundaries. Specifically, the awardee, Texas A&M University, will introduce a class of nonstationary models that can flexibly characterize potentially heterogeneous spatial dependence while...
- This federal Project Grant award of $239,420.00 from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research to advance statistical methods for analyzing complex spatial data. The investigators at the Colorado School of Mines aim to develop novel frequency domain resampling techniques that can effectively handle irregularly spaced spatial data, a common challenge in fields like geosciences and environmental science. The new methods will...
This $199,944 federal Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) aims to develop advanced explainable spatial machine learning models and algorithms for clustering and predicting complex socioeconomic data. The project will create innovative, interpretable spatial machine learning methods applicable to a range of real-world problems in fields like public safety, social sciences, and advanced manufacturing. Key deliverables include a new Bayesian random graph partition model for handling diverse socioeconomic data, a spatial nonparametric regression model using ensemble decision trees, and visualization software with a spatial Shapley value framework to enhance model interpretability. The award was granted to Texas A&M University, a leading research institution with extensive experience supporting federal agencies across scientific and technical domains. No sub-awards are planned under this project, which has an ultimate completion date of August 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $199.9k | 8/26/25 |