Project Grant 2413549
Award Date
8/1/24
Completion Date
7/31/27
Dollars Obligated
$132K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Chicago, IL 60612, USA
Similar Awards
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,...
This $250,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of algorithms for real-time dynamic risk identification and monitoring of streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The key objectives are to create a unified framework for dynamic risk detection that can be incorporated into...
This National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049) Project Grant of $229,710 awarded to the University of North Carolina at Charlotte will develop novel semiparametric statistical models and algorithms to enable more effective analysis of censored data, with applications in personalized medicine. The project aims to extend existing transformation models in survival analysis to better handle challenging data structures. Additionally, it will...
This $300,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop new nonparametric theory and methods for analyzing censored data. The key objectives are to: Establish a distributional theory for spline-based estimates in various censored data models, which is currently lacking in the literature. Investigate the use of deep neural networks with full likelihood-based...
This $160,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences, under the CFDA Program 47.049 Mathematical and Physical Sciences, will develop a new, efficient, and well-justified methodology called ABCEL for data-driven statistical inference on complex models. The ABCEL procedure will enable rigorous analysis of processes with intractable likelihoods, such as phylogenetic trees, dynamical systems, and exponential random graph models, which are...
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 $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
The National Science Foundation awarded $150,000 to Regents of the University of California at Riverside under the Mathematical and Physical Sciences program (CFDA 47.049) from July 1, 2023 to June 30, 2026. The Project Grant funding will support research to develop new statistical methodologies and deep learning techniques for uniformly estimating causal effects of continuous treatments using large observational health data sets. Specifically, the university will design neural network...
This $200,000 project grant was awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program to the University of Delaware. The grant will support the development of new nonparametric learning methods for high-dimensional survival data analysis, with applications in causal inference and sequential decision-making problems. The research aims to advance the state-of-the-art in areas like medical risk factor discovery, personalized treatment...
This $121,153 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop advanced statistical methods for analyzing complex spatial point process data. The key products and services to be delivered include: Developing nonparametric Bayesian models to reveal hidden spatial homogeneity and heterogeneity within and across univariate and multivariate spatial...