This $375,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to advance Bayesian inference methods for computational models in the behavioral sciences. The 3-year project, awarded to Rensselaer Polytechnic Institute, aims to develop a framework for efficient Bayesian inference that enables researchers to quickly fit, criticize, and adapt complex mechanistic models. The research will make...
This Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...
This five-year, $410,524 National Science Foundation project grant supports research and educational outreach activities aimed at advancing efficient global optimization methods under uncertainty. Funded through NSF's Engineering Directorate under the CFDA 47.041 program, the grantee will develop novel Bayesian optimization algorithms that exploit known problem structures to overcome efficiency barriers in optimizing extremely expensive functions. Specific research goals include optimizing...
This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
This Project Grant award, in the amount of $349,880, was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The award will support research to advance Bayesian hierarchical methods for inverse problems, with applications in areas such as medical imaging, large language models, and infrastructure monitoring. The work will combine hierarchical Bayesian techniques with novel data science approaches to develop fast,...
This National Science Foundation (NSF) Project Grant award of $384,214 to Northeastern University will develop statistical inference methods that leverage advanced techniques like reinforcement learning, Bayesian statistics, and machine learning. The project aims to 1) incorporate expert knowledge into the modeling process without requiring expert oversight, and 2) systematize data collection for accurate inference of complex systems and processes. The proposed approaches will be applied in...
The National Science Foundation (NSF) awarded a $599,337 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Regents of the University of California, doing business as the University of California, Berkeley. The award supports research to investigate Bayesian estimation and constraint satisfaction problems, with a focus on determining the minimum number of observations needed to efficiently infer hidden quantities using powerful algorithmic...
This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...
This National Science Foundation Project Grant of $220,000 supports research into statistical modeling methods for large, complex datasets. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of California, San Francisco will develop new Bayesian regression techniques using random data compression matrices. These approaches aim to enable efficient, scalable inference and prediction from high-dimensional biomedical data sources like brain imaging, genetics,...
The Ohio State University received a $199,999 Project Grant award from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The award will fund research from July 1, 2023 to June 30, 2026 to develop new theoretical and computational techniques for reducing computational costs and accurately controlling statistical risks in the analysis of u-statistics. The University will conduct innovative analysis to...