Project Grant 2208385
- This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $171,089 to Old Dominion University Research Foundation for collaborative research developing computationally efficient methods for non-smooth and non-convex optimization by exploring sparsity structures in large data sets. The research aims to address critical issues in non-smooth, non-convex optimization arising from sparse modeling of data used in machine learning and sparse...
- 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...
- This National Science Foundation project grant of $250,000 will fund research at Rensselaer Polytechnic Institute from July 2022 to June 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports the development of accelerated distributed stochastic optimization methods and applications in machine learning. Specifically, the grantee will design fast-convergent and communication-efficient optimization algorithms with theoretical guarantees for solving...
- This National Science Foundation project grant of $199,968 supports collaborative research at Tufts University from September 2022 through August 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The research aims to advance theory and computation for structured sensing problems involving low-rank matrix recovery from deterministically structured measurements. Specifically, the project will study scalable non-convex optimization methods for generalized matrix completion...
- This $193,155 three-year Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at Brigham Young University to develop a mathematical framework describing how sparse network structures can effectively process information and aggregate it in ubiquitous real-world network patterns. The project aims to advance understanding of how network topology impacts machine learning algorithms' ability to learn from data, starting with...
- The National Science Foundation awarded The Ohio State University a $110,335 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to conduct research on deep sparse models from August 1, 2022 to June 30, 2023. Specifically, the university will advance the theoretical understanding of deep convolutional neural networks through analyzing and developing algorithms for multi-layered convolutional sparse models. Researchers will derive provable and...
- 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...
- Federal Grant Award Summary Cornell University's Office of Sponsored Programs received a $210,000 project grant from the National Science Foundation's Division of Mathematical Sciences (CFDA 47.049) for the period July 1, 2025 through June 30, 2028. This three-year award funds research into the structural and algorithmic characteristics of high-dimensional probability models, with particular focus on spin systems and neural networks. The project aims to develop efficient algorithms for...
- This three-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program will support the development of new optimization approaches for machine learning problems. The $600,000 award to the University of Wisconsin-Madison beginning October 1, 2022 will advance optimization algorithms and analysis techniques for convex-concave minimax problems incorporating sparsity or regularity....
- Syracuse University was awarded a $123,073 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences. The award is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049) and will fund research titled "CONVEX BODY SHAPE RECOVERY VIA GEOMETRIC MEASURES AND INEQUALITIES" from July 1, 2021 through May 31, 2023. Under this award, Syracuse University researchers will develop new techniques using geometric measures and inequalities to...
This $155,783 National Science Foundation project grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds collaborative research at Syracuse University to develop computationally efficient methods for non-smooth and non-convex optimization by exploring sparsity structures in large data sets. The research aims to address critical issues in non-smooth, non-convex optimization arising from sparse modeling of data for applications including machine learning and sparse image/signal restoration. The investigators will develop proper sparse regularization models and sparsity-promoting functions for regularization. They will also construct appropriate bases/transforms to compel solutions to be sparse. For machine learning, the researchers will build reproducing kernel Banach spaces to represent complex data geometric and topological structures for improved outcomes. They will establish representer theorems for the resulting learning methods and employ geometric features to induce sparsity of solutions. Construction of bases or transforms via machine learning from data is expected to lead to improved image/signal restoration methods. The period of performance is August 15, 2022 through July 31, 2025.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $155.8k | 8/2/22 |