Project Grant 2515789
- This $124,487 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences Program (CFDA 47.049) will fund research to develop advanced statistical tools and techniques for analyzing large-scale biomedical imaging data. The research aims to overcome key challenges in causal analysis of imaging outcomes, including handling computational demands and addressing issues like unmeasured confounding and population heterogeneity. The resulting data analytics tools are...
- This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $119,994 to the University of Iowa to develop statistical models for analyzing longitudinal biomedical data. The project aims to address challenges in understanding changes in biological markers over time, using multidimensional data. The research team will collaborate with neuroscientists to adapt and enhance modeling approaches for analyzing data from mouse studies. The...
- This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) in the amount of $197,007 is supporting collaborative research to develop new statistical theories and methodologies for tackling issues related to false discovery rate control in regression analysis. The research aims to provide novel statistical tools for analyzing complex data from diverse scientific domains such as brain imaging, genome-wide association studies, and atmospheric...
- 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 $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 $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 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 National Science Foundation (NSF) awarded a $108,000 Project Grant under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to the University of Illinois for the collaborative research project "Distributional Balancing Methods for Advancing Causal Inference in Complex Settings". The project aims to develop advanced statistical methodologies that improve the reliability of causal conclusions from complex, observational data. Specifically, it will enhance...
- 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 federal Project Grant award of $175,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new theoretical frameworks for self-supervised representation learning and their applications in biomedical research. The project aims to advance the theoretical foundations of this machine learning approach and expand its use in biomedical domains where labeled data is scarce. Key anticipated outcomes include new...
This $125,627 federal Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), will support collaborative research to develop advanced statistical tools for analyzing large-scale biomedical imaging data. The research aims to overcome key challenges in causal analysis of imaging outcomes, including addressing issues such as unmeasured confounding and population heterogeneity. The project, led by Iowa State University, will create a general functional data analysis (FDA) framework to facilitate more accurate causal insights from observational imaging studies. The research outputs will include new data analytics techniques, freely available software tools, and curriculum development to benefit the broader research community and support STEM education. This work aligns with national interests in scientific innovation and evidence-based decision-making, with potential applications across fields like aging research, digital health, and plant science.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $125.6k | 7/23/25 |