Project Grant 2212419
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...
- The National Science Foundation awarded a $500,000 Project Grant to the Texas A&M Engineering Experiment Station to advance optimization for threshold-agnostic fair artificial intelligence systems under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year project aims to develop scalable stochastic optimization algorithms and novel threshold-agnostic fairness measures to directly optimize machine learning models for fairness without reliance on...
- The National Science Foundation awarded a $169,977 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "ROBUST AND EFFICIENT STATISTICAL INFERENCE IN LARGE SCALE SEMI-SUPERVISED SETTINGS." The three-year award, which runs from August 1, 2021 through July 31, 2024, will fund the development of statistical methods to enable robust and efficient inference on large, semi-supervised datasets. As the prime...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $600,000 Project Grant to Carnegie Mellon University (CMU) under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports a 3-year research project focused on developing rigorous statistical methods for Optimal Transport, a mathematical technique used to combine data from different scientific domains and mitigate unintended biases in algorithms. The project has three main thrusts: 1)...
- 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...
- This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
- This $113,834 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports collaborative research at North Carolina State University to develop efficient algorithms for optimal transport in geometric settings. The project aims to advance the theoretical underpinnings of optimal transport, a powerful tool for comparing probability distributions, and bridge the gap between theory and practice of algorithms. By...
- The National Science Foundation Division of Mathematical Sciences awarded Texas A&M Engineering Experiment Station a $180,000 Project Grant under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) from August 1, 2023 through July 31, 2026. The award will support research to develop a systematic approach for constructing deep Bayesian neural networks that are both computationally efficient and amenable to model designs. The research is expected to lead to...
- This $329,432 federal Project Grant award, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), aims to enhance the research and training capacity in scientific machine learning (SciML) for undergraduate students at Texas A&M University-San Antonio (A&M-SA), a Hispanic-serving and primarily undergraduate institution. The key products and services to be delivered under this grant include: Formalizing a research partnership between...
- This $236,099 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is supporting research to develop robust optimization and machine learning algorithms capable of handling dynamic and uncertain data environments. The research aims to advance optimization techniques for fundamental supervised learning tasks, yielding computationally and data-efficient algorithms with provable error guarantees. This work will...
This $600,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund the development of new statistical methods and machine learning frameworks for measuring differences between probability distributions. The grant supports the establishment of conditional transport as a novel statistical distance metric to address limitations of existing distribution comparison methods. It will also develop an efficient distribution-based learning framework using conditional transport and approximation algorithms. This framework aims to advance machine learning and artificial intelligence fundamental research through new models and inference algorithms. Specific applications targeted include inverse materials design and multi-omics data analysis to enable reliable uncertainty quantification for critical decision making. The work will be performed by Texas A&M Engineering Experiment Station over a four-year period through September 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 8/18/22 |