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...
Clemson University was awarded a $361,459 Project Grant from the National Science Foundation to support research investigating mathematical approaches for designing efficient mechanisms for early-stage clinical trials with patient choice. The funding supports a three-year project period from September 1, 2023 through August 31, 2026 under the NSF Engineering (ENG) program (CFDA 47.041). The University will develop new dynamic dose-selection game models integrating evolving patient preferences...
This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $349,993 to the University of Chicago aims to advance the methodology and practical implementation of adaptive experiments. The three-year project, starting September 1, 2024, will develop new statistical methods for sample size calculations and optimal treatment assignment in adaptive settings. It will also establish a comprehensive framework to help applied researchers navigate...
This $250,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) supports collaborative research to develop advanced statistical tools for efficient integrative analysis of electronic health records and genomics data. The key goals are to: 1) devise data-driven algorithms with theoretical optimality guarantees for transfer learning in areas like high-dimensional...
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...
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...
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...
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 $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...