Project Grant 2304767

Award Date 11/1/22
Completion Date 7/31/25
Dollars Obligated $150K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Fairfax, VA, USA
Similar Awards
The National Science Foundation awarded a $150,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Illinois for the period of August 15, 2022 through July 31, 2025. The grant funds research to develop and study optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the principal investigators will first develop and analyze subdata selection for clusterwise linear...
This $192,372 Project Grant from the National Science Foundation Directorate for Mathematical and Physical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new methods to systematically explore and predict material microstructures using artificial intelligence techniques. The awardee, George Mason University, will adapt leading data science and machine learning methods to discover a practical representation of microstructure state space that...
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,...
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 $200,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) aims to develop novel feature selection techniques for supervised and unsupervised machine learning models. The research will focus on the "knockoff method" for identifying key predictive features while controlling false discoveries, incorporating microbiome data structures, handling missing values, and...
This $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
This $200,000 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of new statistical methods that incorporate qualitative constraints into semi-parametric models. The awardee, Carnegie Mellon University, will work to create general non-parametric regression estimators that account for subject matter constraints and adapt to the smoothness of the underlying data. Researchers will also explore approaches for...
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...
This $299,999 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds research into distance-based statistical methods for analyzing complex, high-dimensional data. The Washington University is the primary awardee, with work conducted from Oct. 2021 through Jun. 2024. The University of Pennsylvania serves as a subawardee, contributing to study design, implementation, analysis and manuscripts through the work of Dr. Bhaswar B....
This Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences, CFDA 47.049 Mathematical and Physical Sciences, provides $225,000 in funding to Carnegie Mellon University from September 1, 2023 to August 31, 2026. The award supports research on feature selection techniques for high-dimensional data analysis in several challenging areas: Expanding a large-scale dataset on statisticians' publications from 1971-2015 to 1971-2025 to enable network analysis...

This $149,961 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support research into optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the awardee, George Mason University, will develop and study subdata selection frameworks and methods based on clusterwise linear regression and logistic-normal mixture models. Information-based optimal subdata selection approaches will be created first for continuous and then binary response variables, with their statistical properties studied and efficient algorithms developed for incorporation into an R software package. Research findings will also be disseminated through graduate courses in large-scale data analysis and collaborations in public health, biomedical science, and business. The award period runs from November 1, 2022 to July 31, 2025.

Generated 1/6/24, 8:50 PM