Project Grant 2209685
Award Date 9/1/22
Completion Date 8/31/26
Dollars Obligated $399K
Federal Agency
Awardee
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
New Haven, CT 06520, USA
Similar Awards
- The National Science Foundation (NSF) awarded a $498,229 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to Yale University. The grant will fund research to develop new mathematical and machine learning techniques for analyzing complex, high-dimensional biomedical data such as single-cell sequencing and gene regulatory networks. Key research thrusts include creating data geometric features and neural network models to characterize point cloud data, preserving...
- 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 National Science Foundation project grant award of $120,000 supports research into integrative heterogeneous learning for intensive complex longitudinal data under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). Specifically, the University of Illinois will develop new statistical methods and computational tools to address practical biomedical applications involving high heterogeneity and large numbers of decision stages. Key areas of focus include identifying...
- Yale University was awarded a $299,993 project grant from the National Science Foundation Division of Mathematical Sciences on July 1, 2021 to develop measures of heterogeneity for complex data objects. The project directly supports the goals of the NSF's Mathematical and Physical Sciences program (CFDA 47.049) to promote progress in these fields and strengthen the nation's scientific enterprise through increased knowledge and understanding of major problems. Specifically, Yale researchers...
- 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...
- 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...
- 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 $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...
- 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,...
- This $196,673 federal Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program, supports collaborative research at Yale University to develop a predictive framework for understanding how the physical microenvironment influences 3D genome organization and gene expression. The project will integrate super-resolution microscopy, genomics, biophysical modeling, and machine learning to decode the relationship between the...