Project Grant 2208340
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $322,730 Project Grant to the University of Texas at Austin from September 1, 2023 to August 31, 2026. The grant is funded under NSF's Mathematical and Physical Sciences Program (CFDA 47.049) and aims to develop techniques for assessing the accuracy of randomized algorithms used to solve fundamental linear algebraic equations in computational science. The project will focus on improving the speed and robustness...
- This $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
- 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 National Science Foundation Project Grant of $229,021 awarded on August 1, 2022 will support research at the University of Texas at Austin to develop mathematical frameworks in optimal transport applications to probability, machine learning, and kinetic theory through July 31, 2025. Under the Mathematical and Physical Sciences program (CFDA 47.049), the investigator will advance understanding of stochastic modeling, artificial intelligence algorithms, and kinetic theory by exploiting...
- The National Science Foundation Division of Mathematical Sciences awarded a $650,000 Project Grant to the University of Texas at Austin to support the CRCNS RESEARCH PROJECT: MULTIPLY AND CONQUER: REPLICA-MEAN-FIELD LIMIT FOR NEURAL NETWORKS from September 15, 2021 through August 31, 2024. This award will fund research under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these fields and strengthen the nation's scientific enterprise....
- Federal Project Grant Award Summary The University of Texas at Austin received a $320,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), effective August 1, 2026, with completion targeted for July 31, 2029. This award funds the development of fast randomized algorithms designed to solve large-scale computational problems in scientific computing and engineering applications. The project...
- Award Summary The University of Texas at Austin received a $399,998 Project Grant from the National Science Foundation's Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) for the period August 1, 2025 through July 31, 2028. The award supports research on "Numerical Scheme-Guided Deep Learning for Scientific Computing," which develops innovative algorithms that integrate classical numerical schemes with deep learning paradigms to...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
- Project Grant Summary The University of Texas at Austin received a $175,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) effective August 15, 2025, with completion anticipated by July 31, 2028. This award funds the development of novel Bayesian hierarchical frameworks designed to address measurement error problems in complex multivariate data analysis. The project delivers advanced...
- This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program totaling $449,995 supported research at the University of Texas at Austin from July 15, 2022 to June 30, 2025. The research aims to develop machine learning and parallel-in-time algorithms to efficiently simulate multiscale dynamical systems, reducing overall computation time for applications in physical science and engineering. Specifically, the researchers will construct effective solution...
This Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $429,148 to the University of Texas at Austin for collaborative research on randomized feature methods for modeling and dynamics from September 1, 2022 to August 31, 2025. The research aims to develop consistent and theoretically validated machine learning algorithms for high-stakes decisions by studying randomized feature networks as an alternative to neural networks for high-dimensional function approximation. Researchers will introduce new algorithms based on randomized features with adaptive thresholding to improve accuracy without overfitting for scientific modeling and high-dimensional dynamical systems. The grant also supports research training for students.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $214.6k | 6/3/22 |