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 from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $180,000 to the University of North Carolina at Chapel Hill to develop an efficient statistical framework for improving individualized treatment decisions in personalized medicine. The key products and services to be delivered include: 1) Advancing methods for estimating optimal individualized treatment rules that can handle complex relationships among...
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 $213,462 National Science Foundation award under the Mathematical and Physical Sciences program will support the development of new statistical methods for analyzing functional medical data with skewness and outliers. Specifically, the University of North Carolina at Charlotte will develop dimension reduction techniques for quantile regression to analyze functional magnetic resonance imaging and electroencephalogram data related to attention deficit hyperactivity disorder and alcoholism....
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $250,000 to The Trustees of the University of Pennsylvania to develop advanced statistical methods for integrating and analyzing large-scale data from multiple sources, such as electronic health records and genomics data. The project aims to devise new data-driven algorithms with theoretical optimality guarantees for transfer learning, as well as adversarially...
The National Science Foundation awarded North Carolina State University a $200,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "Offline Statistical Reinforcement Learning with Applications in Precision Health" from August 15, 2021 through July 31, 2024. The university will apply statistical reinforcement learning techniques without an interactive environment to develop models with applications in precision health. The...
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 from the National Science Foundation Division of Mathematical Sciences supports the development of a generalizable data framework to enable precision radiotherapy for individual cancer patients. Funded at $104,016 under the Mathematical and Physical Sciences program (CFDA 47.049), the award will support collaborative research between Jackson Laboratory and other organizations to build and validate a deep reinforcement learning model using multimodal imaging data from cancer...
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 from the National Science Foundation Division of Mathematical Sciences provides $261,814 to Duke University to support research activities under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). Specifically, the award will fund the development of new methods and theories for optimal decision-making and valid statistical inference in high-dimensional settings, with a focus on applications in personalized medicine. Key deliverables include new...
This National Science Foundation project grant of $260,000 supports research at the University of North Carolina at Chapel Hill to develop statistical analysis frameworks for precision medicine incorporating abundant data features. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the three-year award running from August 2022 to July 2025 will adapt semi-parametric and reinforcement learning methods to precision medicine scenarios involving medical images, genetic information, demographic data, and other factors.
Specifically, the university researchers will conduct theoretical development of 1) a functional individualized treatment regime incorporating multi-dimensional imaging features, 2) a generalized functional regime allowing discrete responses, and 3) functional Q-learning for multi-stage decision settings. They will develop efficient algorithms and apply the resulting software tools to real-world data. In addition to advancing statistical and computational methods for precision medicine, this work seeks to provide early disease diagnosis and treatment guidance utilizing diverse clinical data sources.