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 $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 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 National Science Foundation (NSF) awarded a $237,438 Project Grant to Purdue University under the Social, Behavioral, and Economic Sciences grant program (CFDA 47.075) to advance statistical inference on dynamic systems. The 3-year project will leverage deep learning and statistical modeling to enhance the efficiency, accuracy, and interpretability of time-series analysis across various domains. The research will introduce a new neural inference framework for estimating and inferring dynamic...
This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
This National Science Foundation (NSF) Project Grant under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $375,000 to Purdue University from April 2025 to March 2028 to develop statistical methods and tools for analyzing relationships between variables in complex psychometric studies. The research aims to enable likelihood-based inference for exponential family graphical models, which are often computationally intractable, in order to produce efficient and...
This federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences program (CFDA 47.075) provides $375,000.00 to Iowa State University of Science and Technology to develop statistical and machine learning tools for data integration and data fusion. The project aims to enhance the analysis of complex survey data with big data sources, as well as improve scientific conclusions drawn from multiple datasets. Key activities include research on mass...
This $131,615 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences, under the CFDA program 47.049 Mathematical and Physical Sciences, aims to develop novel Bayesian statistical models for analyzing complex high-dimensional health data. The research will focus on creating improved joint models that can leverage information from longitudinal measurements, such as clinical data and biomarkers, to better predict time-to-event outcomes like disease...
The National Science Foundation (NSF), through its Division of Mathematical Sciences, awarded a $225,000 Project Grant to The Leland Stanford Junior University (Stanford University) to develop new algorithms for Bayesian computation. The grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), aims to address challenges in Bayesian inference for complex statistical models, such as hidden Markov models with continuous variables and models with intractable likelihood...
The National Science Foundation (NSF) Social, Behavioral, and Economic Sciences program awarded a $214,625 Project Grant to the San Diego State University Research Foundation (SDSURF) to develop new model-based estimators for improving the precision of population parameter estimates in small area survey sampling. The project will create clustered coefficient regression models that can better handle heterogeneous relationships between survey variables and auxiliary data, compared to traditional...