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 Project Grant award of $175,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at the University of California, Davis (UC Davis) to develop new statistical and computational methods for analyzing high-dimensional, noisy, and dynamically changing datasets. The key focus areas of the project include: (1) analyzing the robustness of manifold and deep learning algorithms for complex data; (2) developing statistical...
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 $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,...
The National Science Foundation awarded a $252,937 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to the University of Chicago. The purpose of this 3-year grant, which runs from September 1, 2023 to August 31, 2026, is to enhance statistical methods for analyzing temporally observed, multi-sample data in fields such as environmental science, epidemiology, and economics. The research team will develop innovative approaches to estimate and infer trends in data...
This $375,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at the Massachusetts Institute of Technology (MIT) to advance the understanding of statistical analysis within a broader environment with multiple stakeholders. The project aims to develop statistical protocols that are robust to the behavior of different stakeholders who may have different aims, in order to enable more reliable...
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...
This National Science Foundation (NSF) Project Grant award for $106,291, under the Mathematical and Physical Sciences program (CFDA 47.049), will support research to extend classical extreme value theory to models with interdependent numerical values and mean-field interaction. The project aims to study the convergence of upper and intermediate order statistics of certain systems of stochastic differential equations as their size grows, with applications in finance, medicine, and other...
This $170,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports fundamental research on experimental design and uncertainty quantification frameworks for complex systems. The research aims to develop new statistical surrogate models and sequential experimental algorithms to enhance the efficiency and effectiveness of information collection and decision-making for complex systems in...
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...