Project Grant 2208314
- The University of Florida was awarded a $125,701 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant will support the development of a mathematical foundation for novel artificial intelligence learning algorithms with applications to biology and engineering. Specifically, researchers will establish a theoretical framework for the minimax optimization of machine learning...
- This $307,266 Project Grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, aims to develop effective computational methods for training neural networks. The project focuses on establishing a novel Exploration-Exploitation-Determination (EED) framework to improve the training performance of neural networks, which are a core component of modern artificial intelligence (AI) models. Key objectives include:...
- This $255,237 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research on acceleration and preconditioning methods to enhance the efficiency of deep learning models. The research team at the University of Minnesota will investigate techniques leveraging accelerators and preconditioners to speed up the computationally intensive training process for large AI models. The project aims to develop new...
- The National Science Foundation awarded a $600,000 Project Grant under its Computer and Information Science and Engineering program (CFDA 47.070) to the University of Florida to support research on the design and analysis of recursive algorithms with applications in machine learning, optimization, and reinforcement learning. The research aims to develop new techniques to ensure stability and accelerate the convergence of stochastic approximation algorithms, which are critical to training...
- This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
- 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...
- 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 award provides $399,998 to the University of California, Los Angeles under the Mathematical and Physical Sciences program (CFDA 47.049) for the period of July 1, 2022 through June 30, 2025. The project will develop new algorithms and mathematical theory for multi-agent sequential deep learning using insights from ordinary and partial differential equations. Researchers will integrate advances in neural ordinary differential equations with graph networks to build...
- The National Science Foundation awarded a $134,150 CAREER Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the University of Pennsylvania. The purpose of this award is to advance the design, analysis, and deployment of rigorously justified nonconvex optimization algorithms. This research aims to lay a mathematical foundation for understanding why nonconvex statistical estimation and learning algorithms work and how they can be improved, with the...
This National Science Foundation Project Grant award of $335,978 provides funding from August 1, 2022 through July 31, 2025 to develop novel robust optimization algorithms for deep learning under the federal Mathematical and Physical Sciences grant program (CFDA 47.049). Specifically, the University of Kentucky Research Foundation will create two new classes of optimization algorithms building on preconditioning and conjugate gradient methods but incorporating ideas from normalization and momentum methods. The algorithms aim to more efficiently train deep learning models by addressing difficulties in optimizing high complexity, nonlinear neural networks. As part of the work, the Foundation will apply the new algorithms to chemical representations in drug design and Bayesian inference for uncertainty quantification. Graduate and undergraduate students will receive training in deep learning research. Any software developed will be made freely available, advancing the Foundation's goal under the NSF program to strengthen the nation's scientific enterprise.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $336.0k | 8/5/22 |