Project Grant 2413405
- This National Science Foundation Project Grant award of $540,000 provides funding from July 1, 2022 to June 30, 2025 to address new challenges in statistical inference with regularized optimal transport. The award is made under the Mathematical and Physical Sciences program (CFDA 47.049) to Cornell University to explore modern regularization techniques for optimal transport distances and develop a comprehensive statistical theory to facilitate principled inference in high dimensions....
- This three-year Project Grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $399,998, will fund research at Cornell University's Office of Sponsored Programs on statistical optimal transport in high dimensional mixtures. Specifically, the researchers will define a new "sketched Wasserstein distance" measure to compare and analyze high-dimensional datasets arising in fields such as linguistics, computational biology, and particle physics....
- 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 $250,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research in two areas: (1) regularized optimal transport theory and (2) price impact modeling in financial markets. For the first part, the project investigates the mathematical foundations and theoretical guarantees for sparsity in quadratically regularized optimal transport. This builds on prior work on entropically regularized optimal transport....
- This National Science Foundation Project Grant of $315,519 supports research at the University of California, Los Angeles from July 2022 to June 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The award funds research investigating challenging problems in optimal transport theory and its applications in fields including partial differential equations, geometry, probability, and machine learning. Key areas of focus include developing the theory to analyze games with large...
- 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 $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 awarded a $239,999 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Georgia Tech Research Corporation from September 15, 2022 to August 31, 2025. The grant funds research to develop mathematical theories and efficient algorithms for multi-marginal optimal transport problems with graphical costs. Specifically, the principal investigator will establish a unified framework for these problems by merging concepts from optimal...
- This $300,000 federal Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences will fund research to develop new mathematical techniques for optimization in the context of big data and contemporary data science challenges. The principal investigator at Cornell University will lead this 3-year project, which aims to transform the design and analysis of optimization algorithms across diverse fields including machine learning, statistics, and control...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $184,593 project grant to Lafayette College in Easton, Pennsylvania. The grant, under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), will fund collaborative research to develop theoretical tools and efficient numerical methods for solving the optimal transport problem. This mathematical problem seeks to minimize the total cost of transporting mass from one location to another, with applications...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will provide $160,000 to Cornell University from July 1, 2024 to June 30, 2027 to advance statistical optimal transport theory and its applications. The research project aims to develop new computational and statistical methods for optimal transport, and apply them to various data analysis tasks like clustering, generative modeling, and dimension reduction for measure-valued and multivariate data. The work will produce novel technical tools integrating theory, empirical process analysis, and partial differential equations to enable robust optimal transport-based inferential methods. The project is expected to advance the fundamental understanding of optimal transport as a statistical tool, as well as inspire new high-impact applications across disciplines. No sub-awards are planned for this project.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $160.0k | 6/18/24 |