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...
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 of human cognition and behavior. The key products or services to be delivered under this 3-year award include:
Developing a generalized framework for amortized Bayesian inference that can support a wider range of complex computational models, experimental designs,...
This federal Project Grant award of $249,999.00 from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program aims to enhance policy decisions based on statistical models that are incorrectly specified but closely fitting. The research project will develop new equation-by-equation maximum likelihood estimation methods for structural equation models involving latent variables to provide more accurate inferences for correctly specified equations...
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 Project Grant award of $194,675 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to develop new econometric and statistical methods that allow researchers to draw valid conclusions from complex data in a variety of social, behavioral, and medical science settings. The key products and services to be delivered include:
Developing a generally applicable reparameterization procedure for informative...
This $226,874 Project Grant awarded by the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical tools for modeling and analyzing dependent data. The project, to be conducted by Cornell University, will focus on advancing methods for handling data dependencies in areas such as local elections, inflation modeling, spatial patterns, and environmental monitoring data. The research aims to provide...
The National Science Foundation Division of Social and Economic Science awarded Purdue University a $103,752 Project Grant under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) to conduct collaborative research from January 1, 2022 to December 31, 2023. The research aims to explain differential success in biodiversity knowledge commons. As the sole awardee, Purdue University will leverage the funding to study how certain communities effectively share biodiversity data as a...
This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $300,000 to Florida State University (FSU) supports the development of a new class of latent variable models for network data. The research aims to create models that can capture essential characteristics observed in network data from various disciplines, including the social and life sciences. Key activities include integrating and extending existing approaches to modeling...
This $192,632 Project Grant awarded by the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences Directorate (CFDA 47.075) supports interdisciplinary research at Purdue University exploring how to improve decision-making processes. The project aims to develop a novel theory and operational framework for understanding how individuals and organizations weigh information sources and aggregate disparate data to reach conclusions, with applications in areas like user...
This $229,461 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations within big data applications, particularly in areas like cancer research where hidden tree-structure graphs could provide insights into disease progression. The research involves creating advanced...