Project Grant FA23862314100

This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $109,999 to support research at the University of Maryland Baltimore County on novel modeling and Bayesian analysis of high-dimensional time series data. The three-year award beginning September 2022 will fund the development of a framework to represent multi-dimensional time series data as independent stationary latent processes modeled with unspecified spectral densities. The...

This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $270,000 in funding to Virginia Polytechnic Institute & State University (Virginia Tech) to develop new Bayesian statistical methodologies that incorporate heavy-tailed probability distributions. The overarching goal is to build scalable statistical methods that can address significant challenges in three application areas: independent component...

This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $359,940, will support the development of new statistical models and algorithms for analyzing large, spatially-dependent data sets collected from complex domains with irregular boundaries. Specifically, the awardee, Texas A&M University, will introduce a class of nonstationary models that can flexibly characterize potentially heterogeneous spatial dependence while...

This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $180,000 to North Carolina State University from September 1, 2022 through August 31, 2025. The funding supports research to develop novel modeling and Bayesian analysis techniques for high-dimensional time series data. Specifically, the awardees will create a framework to represent multi-dimensional time series data as independent latent time series, allowing for more accurate...

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...

This Project Grant award of $375,000 from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to advance Bayesian inference for the analysis of complex human behavioral data. The project aims to develop a new framework for efficient Bayesian inference that enables researchers across the social and behavioral sciences to quickly develop, fit, and adapt complex computational models. Key objectives include: Generalizing the scope...

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...

The National Science Foundation awarded a 3-year, $300,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Regents of the University of California, Berkeley. The grant funds the development of a software infrastructure to facilitate the use of two cutting-edge Bayesian sampling methods - Preconditioned Optimized Convex Monte Carlo (POCOMC) and Microcanonical Hamiltonian Monte Carlo - by a range of scientists across disciplines such as astronomy, physics,...

The National Science Foundation (NSF) awarded a $219,268 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Chicago. The grant, effective July 1, 2024 through June 30, 2027, will support collaborative research on "Statistical Inference for High Dimensional and High Frequency Data: Contiguity, Matrix Decompositions, Uncertainty Quantification." The research aims to develop advanced mathematical and statistical methodologies to extract...

This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $287,594 to support collaborative research addressing challenges in learning and inference from large-dimensional data. The awardee, The Trustees of the University of Pennsylvania doing business as the Clinical Practices of the University of Pennsylvania, will conduct the research from January 2022...