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...
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 Project Grant of $133,850 supports research at Clemson University under the Mathematical and Physical Sciences program (CFDA 47.049) from August 15, 2022 through July 31, 2025. The award will fund the development of statistical analysis frameworks to incorporate abundant data features, including medical images, genetic information, and other patient characteristics, into precision medicine decision-making tools. Specifically, the researchers will adapt...
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 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 $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 $320,502 Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems is for a collaborative research project titled "Knowledge Discovery from Highly Heterogeneous, Sparse and Private Data in Biomedical Informatics." The research aims to mine healthcare data to identify patients likely to develop chronic conditions like type 2 diabetes and heart failure, and to develop models for opportunistic screening, particularly for...
This $299,999 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds research into distance-based statistical methods for analyzing complex, high-dimensional data. The Washington University is the primary awardee, with work conducted from Oct. 2021 through Jun. 2024. The University of Pennsylvania serves as a subawardee, contributing to study design, implementation, analysis and manuscripts through the work of Dr. Bhaswar B....
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to The Trustees of the University of Pennsylvania - doing business as Clinical Practices of the University of Pennsylvania. This grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), will support the development of improved methodologies for the high-dimensional variable selection problem. The project aims to create innovative statistical techniques to better...
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...