This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will advance the state-of-the-art in nonparametric learning for high-dimensional survival analysis. The $100,000 award, effective July 1, 2024 through June 30, 2027, will support the development of novel supervised embedding and robust nonparametric methods for causal inference and sequential decision-making on high-dimensional survival data. The research aims to provide...
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 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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
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...
This Project Grant award, valued at $250,000.00, was granted by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The award will support a research project titled "Deep Learning for Survival Analysis, Causal Inference, and Conformal Inference" at the University of California, Davis. The key objectives of this 3-year project are to: 1) develop new hypothesis testing procedures for deploying deep learning methods in survival...
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 Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
This $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
This $200,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) aims to develop novel feature selection techniques for supervised and unsupervised machine learning models. The research will focus on the "knockoff method" for identifying key predictive features while controlling false discoveries, incorporating microbiome data structures, handling missing values, and...