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 $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 $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 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 $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 $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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $125,667 Project Grant to Virginia Polytechnic Institute & State University (Virginia Tech) under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund collaborative research to develop new theories and methodologies for multiple hypothesis testing on regression analysis, which is critical for analyzing high-dimensional data in the era of big data. The research project will create...
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 $337,985 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of innovative methods for risk-sensitive statistical learning at Duke University. The research aims to advance decision-making processes in critical fields like medicine, finance, and robotics by incorporating risk assessments to improve outcomes and minimize risks, particularly for a large proportion of the population. Key focus...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Virginia to develop data-driven, multimodal methods for behavior-based epidemiological modeling. The key objectives are to: Improve techniques for deriving meaningful insights from imperfect, real-world sensor data like mobile phones and search engine logs to capture complex human behaviors in real-time. Couple agent-based disease models...