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...
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 Division of Mathematical Sciences awarded $250,000 under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to Stanford University from September 1, 2022 to August 31, 2025. The project grant funding will support research aimed at advancing statistical methods to measure replicability of scientific findings across multiple environments and populations. Specifically, the grantee will develop new approaches leveraging techniques such as...
San Francisco State University was awarded a $245,681 Project Grant from the National Science Foundation Division of Mathematical Sciences' Mathematical and Physical Sciences program (CFDA 47.049) for the project "LEAPS-MPS: STATISTICAL LEARNING ON NEXT GENERATION SEQUENCING OF T/B CELL RECEPTOR REPERTOIRE DATA" from August 15, 2022 through July 31, 2024. Under this award, the University will apply statistical learning techniques to next generation sequencing data of T and B cell...
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 (NSF) Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will provide $350,796 in funding to South Dakota State University (SDSU) from September 1, 2024 to August 31, 2026. The goal of this research is to develop new statistical methods and algorithms for handling "few-shot learning" problems, where there are large datasets divided into many categories but with only a few examples in each category. The research will focus...
The National Science Foundation awarded The Regents of the University of California $330,000 under the Mathematical and Physical Sciences program (CFDA 47.049) to advance theory and methodology for tree-based algorithms in high dimensions from July 2022 to June 2025. The project will analyze the generalization performance of tree-based algorithms on regression models to better understand their inductive bias for different data structures. It will also study a new framework for obtaining...
This $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
This $375,000 Project Grant award, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, aims to advance the understanding of statistical analysis within a broader environment involving multiple stakeholders with different incentives and information asymmetry. The project, led by the Massachusetts Institute of Technology (MIT), seeks to develop statistical protocols that are robust to the strategic behavior of...