Project Grant 2533995
- This $224,994 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a new framework for digital twin modeling of Alzheimer's disease (AD). The project aims to combine clinical data, biomedical research, and advanced computational methods to create personalized digital twins capable of predicting disease progression and evaluating treatment options for individual patients. The key products or...
- This $888,680 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support collaborative research by the University of Maryland Baltimore County (UMBC) on developing digital twin models and data science techniques for studying neurodegenerative diseases like Alzheimer's, Parkinson's, and multiple sclerosis. The 3-year project aims to advance the theoretical foundations, methodological tools, and algorithmic principles of...
- This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $370,774 to the University of Houston System to develop an innovative framework for learning digital twins of human physiology. The goal is to create personalized, data-enabled digital models that can simulate glucose metabolism and help evaluate new treatments and technologies for type 1 diabetes management, without the risks of real-world trials. The research...
- This $200,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to develop the mathematical and statistical foundations for a Digital Twin (DT) system to enhance neurophysiological modeling and uncertainty quantification for individuals with Autism Spectrum Disorder (ASD). The key products and services to be delivered include: Computational models based on Conditional Variational Auto-Encoders (CVAE) and longitudinal CVAE to analyze brain...
- This Federal Project Grant award, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports the development of a first-principles informed, data-enabled predictive digital twin framework for human physiology. The $432,000 award to Arizona State University, a Hispanic-serving institution, will advance techniques for integrating real-world data into physics-based models to create personalized digital representations of human metabolic...
- This National Science Foundation (NSF) Project Grant award to Wake Forest University, under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop a "Neuron Twin" computational system that simulates the human brain to improve understanding and predictions related to Alzheimer's disease. The $501,329 five-year project will leverage deep learning and multiscale modeling to jointly analyze multimodal data, including genetic, neuroimaging, and...
- This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $269,979 to the Santa Fe Institute of Science to conduct collaborative research on developing robust digital twin models that can accurately predict the behavior of complex systems under unexpected conditions. The key objectives are to: Investigate the generalization abilities of digital twins by combining mathematical tools from nonlinear dynamics and machine...
- This $200,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will fund the development of advanced computational methods to create high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. The project aims to deliver: 1) novel machine learning algorithms for accurate digital twin geometry reconstruction from 3D medical images, 2) an efficient inverse method to identify in vivo...
- This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports fundamental research to improve the generalization capabilities of digital twin models for complex systems. The $269,187 grant will enable researchers at Smith College to develop hybrid digital twin architectures that combine physics-based and domain-agnostic components, allowing for improved predictive performance across a range of conditions,...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program aims to improve diabetes care through the development of personalized digital twin models. The $716,195 award to the Research Foundation for the State University of New York (RF SUNY) will fund the creation of virtual patient models that can learn from wearable health sensors and guide real-time insulin delivery using automated medical devices....
This Project Grant award, valued at $175,007, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The award supports the development of a new framework for digital twin modeling of Alzheimer's disease (AD), combining clinical data, biomedical research, and advanced computational methods to enable personalized medicine. The project aims to build a unified modeling framework for population-based and personalized digital twins of AD, using large language models to extract causal networks of AD biomarkers and integrating this with clinical data to generate personalized disease progression predictions. The framework will incorporate conformal prediction techniques to quantify uncertainty and leverage gradient-based learning with language model-guided parameter search to optimize treatment planning under limited data. The digital twin models will be used to simulate disease trajectories and support digital clinical trials, with optimal individualized therapeutic strategies identified through deep reinforcement learning. This work advances the field of personalized medicine and supports interdisciplinary collaboration across artificial intelligence, mathematics, and medicine, while offering new training opportunities for students.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 8/6/25 |