Project Grant 2310942
Award Date
7/1/23
Completion Date
6/30/26
Dollars Obligated
$225K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Madison, WI 53715, USA
Similar Awards
The University of Wisconsin-Madison received a $200,000 project grant award from the National Science Foundation Division of Mathematical Sciences on July 1, 2021 to complete work by June 30, 2024. The award falls under the Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these fields and strengthen the nation's scientific enterprise through increasing knowledge and enhancing understanding of major problems. Specifically, the funding will support...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $299,669 to the University of Wisconsin-Madison to explore and develop new statistical methodology for performing Bayesian inference that allows flexible departures from generative models. The research aims to create a flexible Bayesian non-parametric approach that retains the generative modeling aspects of traditional Bayesian modeling while...
The University of Wisconsin-Madison received a $250,000 project grant award from the National Science Foundation Division of Mathematical Sciences on August 1, 2021. The award is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049) and will support research on "Robust Persistence and Permanence in Biological Interaction Networks" through July 31, 2024. Under the award, UW-Madison researchers will investigate major problems confronting the nation by increasing...
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 $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 $185,163 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of scalable Gaussian process methods for spatial statistics and machine learning. The project aims to create a universal toolbox for highly accurate and computationally efficient Gaussian process modeling to enable improved data analysis, prediction, and uncertainty quantification across diverse applications like carbon monitoring,...
This Project Grant award, valued at $250,000.00, was provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) to the University of Washington. The award supports research aimed at developing new semi-parametric inference techniques that can leverage limited datasets by incorporating additional observations without associated target variables. The research will explore theoretical foundations, new methodological approaches, and applied...
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...
The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
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...