Project Grant 2411198
- This $170,000 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences supports research on developing randomized algorithms for solving inverse problems and quantifying uncertainty in hierarchical Bayesian and dynamical inverse problems. Key goals include: (i) creating efficient algorithms to estimate uncertainty in the hyperparameters that govern Bayesian inverse problems, and (ii) developing new iterative methods leveraging randomization to...
- This $275,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences supports the development of novel approaches to solving inverse problems. The goal is to enhance the accuracy and efficiency of computational methods used in critical applications like electrical impedance tomography, inverse scattering, and cryo-electron microscopy. This research has the potential to accelerate breakthroughs in molecular biology and rapid drug development, directly...
- This three-year $100,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of novel computational methods for large-scale inverse problems, randomized numerical linear algebra, and sparse principal component analysis at Arizona State University. The grant will advance efficient tools for solving large-scale inverse problems in scientific applications experiencing...
- Federal Grant Award Summary North Carolina State University received a $350,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences (CFDA 47.049) effective July 1, 2025, through June 30, 2028. The award supports the development of a geometric framework for stochastic algorithms designed to solve large-scale mathematical models in feasibility and inclusion problems. The research deliverables include foundational principles and methodologies for incorporating...
- This Project Grant award of $160,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at North Carolina State University (NC State) on "Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning." The research aims to develop new statistical methods for reliable uncertainty quantification in high-dimensional structure learning problems that are ubiquitous across the...
- This $100,000 three-year Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to advance numerical methods for large-scale inverse problems, sparse principal component analysis, and their applications. The awardee, Emory University, will collaborate with other institutions to develop novel approaches merging inverse problems, randomized numerical linear algebra, and sparse principal component analysis. This will...
- Federal Grant Award Summary North Carolina State University received a $200,000 Project Grant award from the National Science Foundation's Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), effective September 1, 2025, through August 31, 2028. The award supports fundamental research on inverse problems for hyperbolic partial differential operators, with particular emphasis on advancing the mathematical theory underlying wave-based imaging...
- This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
- This $293,784 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports fundamental and applied research on fluctuating systems, random environments, and stochastic algorithms. The research aims to improve understanding and exploitation of randomness across diverse settings, including materials science, fluid dynamics, and machine learning. Key areas of focus include stochastic homogenization, stochastic partial...
- This $170,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports fundamental research on experimental design and uncertainty quantification frameworks for complex systems. The research aims to develop new statistical surrogate models and sequential experimental algorithms to enhance the efficiency and effectiveness of information collection and decision-making for complex systems in...
This $170,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at North Carolina State University (NC State) to develop new randomized algorithms for solving large-scale inverse problems and quantifying uncertainty in hierarchical Bayesian models. The key research objectives are: (i) to create efficient algorithms for quantifying uncertainty in the hyperparameters governing Bayesian inverse problems, and (ii) to develop innovative randomized iterative methods that can approximate solutions and enable uncertainty quantification for large-scale inverse problems. The project aims to advance the field of randomized algorithms for computational inverse problems, with applications in diverse areas like medical imaging, geophysics, material sciences, homeland security, and astrophysics. The award will also train graduate students in these state-of-the-art randomized algorithms. This 3-year grant, running from Aug 2024 to Jul 2027, does not involve any planned sub-awards.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $170.0k | 5/30/24 |