Project Grant 2208535
- This $250,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by the University of Washington to explore the use of machine learning and artificial intelligence algorithms to augment limited datasets and improve statistical inference. The project will take a three-pronged approach: 1) establishing new semi-parametric efficiency results for semi-supervised learning, 2) developing new and improved...
- 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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research focused on geometric inverse problems and dynamics. The $335,956 award to the University of Washington will center on the study of inverse problems involving transport-type partial differential equations, with applications in areas like X-ray computed tomography, geophysical prospection, and parameter identification for PDEs. The project will...
- The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
- 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...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
- The National Science Foundation (NSF) awarded a $125,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Washington (UW) for a collaborative research project titled "Theory of Causal Learning: How Can We Interpret Results from Complex Machine Learning Algorithms? How Can We Mitigate the Risks Associated with Using Such Models for Policy Decisions?". The project aims to establish a theoretical foundation for causal learning that...
- The National Science Foundation awarded a $250,012 Project Grant to the University of Washington under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) with a performance period from September 15, 2025 to August 31, 2028. The grant supports the development of machine learning tools for data assimilation and model calibration applicable to a broad range of physical systems described by ordinary, differential algebraic, or partial differential equations. The project...
- The National Science Foundation Division of Mathematical Sciences awarded a $200,000 Project Grant to the California Institute of Technology (Caltech) to support the project "LEARNING ALGORITHMS FOR INVERSE PROBLEMS FROM DATA: STATISTICAL AND COMPUTATIONAL FOUNDATIONS" from July 1, 2021 through June 30, 2024. This award falls under the Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these scientific fields and strengthen the nation's...
- This Project Grant award of $349,880 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support research at Case Western Reserve University to advance Bayesian hierarchical models for inverse problems. The project aims to develop fast and robust computational methods known as "inverse solvers" that can efficiently estimate unknown causes from observed consequences, with applications in areas such as medical imaging, large language...
This Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $274,560 to the University of Washington for research titled "Machine Learning for Bayesian Inverse Problems." The award period is from September 1, 2022 through August 31, 2025. The project aims to develop foundational theory and novel computational techniques for applying machine learning methods to quantify uncertainty in solving inverse problems, where unknown parameters are predicted from indirect measurements. Specific activities include establishing a measure-theoretic framework to analyze well-posedness, stability, and consistency for Bayesian inverse problems solved via machine learning. The funding also supports developing new computational approaches using Markov chain Monte Carlo algorithms, data-driven construction of prior information, and variational inference techniques. Outreach activities organized through the University of Washington, including training and retaining young researchers from underrepresented groups in STEM fields, are additionally part of this Project Grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $274.6k | 5/20/22 |