Project Grant 2515084
Award Date 8/15/25
Completion Date 7/31/28
Dollars Obligated $150K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Madison, WI 53715, USA
Similar Awards
- This federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program aims to develop tools and methods to improve decision-making and enhance interpretability in reinforcement learning (RL) algorithms operating in complex, data-limited environments. The $154,999 award supports research to address challenges in ensuring RL systems are statistically robust, interpretable, and socially responsible for real-world...
- 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 $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 $250,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by the University of Washington to explore the use of machine learning and artificial intelligence algorithms to augment limited datasets and improve statistical inference. The project will take a three-pronged approach: 1) establishing new semi-parametric efficiency results for semi-supervised learning, 2) developing new and improved...
- This $140,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI) methods. The project, titled "Collaborative Research: Performance Guaranteed Statistical Learning with Multiple Classes of Models (Guided by PCS)," aims to advance a framework called...
- 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 University of Wisconsin-Madison was awarded a $224,999 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to develop semi-parametric statistical techniques for integrating external data into primary studies across heterogeneous populations. The three-year award running from July 1, 2023 to June 30, 2026 will support research to build a suite of statistically sound methods allowing incorporation of information from one population into...
- This $155,000 project grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) will develop new simulation-based inference (SBI) methods. These innovations aim to empower scientists to make better use of complex models across diverse domains such as genetics, ecology, biology, economics, and psychology, supporting more scalable, efficient, and reliable decision-making. The project will address two core challenges...
- This Project Grant award, valued at $325,000.00, was provided by the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program to the University of Washington. The project develops powerful new tools for understanding complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. The research introduces novel approaches to analyze messy, heterogeneous, and large...
- This $200,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) aims to develop novel feature selection techniques for supervised and unsupervised machine learning models. The research will focus on the "knockoff method" for identifying key predictive features while controlling false discoveries, incorporating microbiome data structures, handling missing values, and...