This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
This $290,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), will support the development of data exploration and visualization tools for Fréchet regression analysis of metric-space valued data at Penn State University. The goal is to systematically develop a set of tools to assist with preliminary data exploration, visualization, model diagnostics, and improved estimation...
The Trustees of the University of Pennsylvania received a $143,659 Project Grant award from the National Science Foundation Division of Mathematical Sciences. The grant was awarded on July 1, 2022 under the Mathematical and Physical Sciences program (CFDA 47.049) to support the research project "COLLABORATIVE RESEARCH: FINE-GRAINED STATISTICAL INFERENCE IN HIGH DIMENSION: ACTIONABLE INFORMATION, BIAS REDUCTION, AND OPTIMALITY." The University will conduct collaborative research through...
This $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
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 $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 $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 $152,997 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Southern California (USC) on computer-intensive statistical inference methods for high-dimensional and massive datasets. The project aims to develop efficient, scalable, and statistically robust inferential procedures for two classical problems - change point detection/identification and computationally-aware statistical...
The National Science Foundation awarded a $275,000 Project Grant to the University of Southern California under the Mathematical and Physical Sciences program (CFDA 47.049) from July 1, 2023 to June 30, 2026. The grant funds the development of new exploratory data analysis and inference methods for complex data that lack fundamental vector space properties. The university will create a practical toolkit of theoretically sound, user-friendly tools to enable common data analysis tasks like...
This $400,000 National Science Foundation Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of statistical methods and machine learning techniques for analyzing complex structured and count data. Over a three-year period ending in August 2025, the University of Washington will advance the state of knowledge in big structured and count data analysis through two tracks of research. The first track will focus on revising and...
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. Bhattacharya and a PhD student. The research aims to advance mathematical theory and computation of nonparametric tests, change points and related problems involving non-Euclidean distances arising in fields including forestry, ecology, marine science and single-cell genomics. It is expected to provide answers to scientific questions and opportunities for graduate student dissertation work while integrating mathematics, science and education.