Project Grant 2152762
- 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...
- 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...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $125,000 Project Grant to the University of Utah for the project "COLLABORATIVE RESEARCH: ATD: FAST ALGORITHMS AND NOVEL CONTINUOUS-DEPTH GRAPH NEURAL NETWORKS FOR THREAT DETECTION" under the Mathematical and Physical Sciences program (CFDA 47.049). The project aims to advance graph neural network (GNN) algorithms for improved accuracy and efficiency in threat detection within multivariate time series...
- This $1,197,878 project grant from the National Science Foundation's Office of Advanced Cyberinfrastructure will support the development of Evolutional Deep Neural Network algorithms for solving high-dimensional partial differential equations. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), this collaboration between U.S. and French researchers aims to accelerate computational predictions of complex phenomena across multiple disciplines. Specifically, the...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program provides $140,889 to Texas A&M University to conduct research connecting machine learning and numerical methods for partial differential equations. The key objectives are to leverage deep learning techniques to improve numerical methods for PDEs, and apply the theoretical understanding of finite element methods to better comprehend the success of deep neural networks....
- This federal Project Grant award of $187,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for data with graph structures. The key products or services to be delivered through this 3-year award (July 1, 2025 to June 30, 2028) include: Developing GNN architectures for solving quadratic...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
- 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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $399,998 to the University of Texas at Austin to develop novel algorithms that integrate classical numerical schemes and deep learning to address complex scientific computing challenges. The project aims to tackle problems in high-dimensional, nonlinear differential equations, long-time simulation of Hamiltonian systems, and boundary integral equations. The...
- 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...
This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $200,000 to support research at the University of Utah developing new deep learning algorithms for sequential and graph data motivated by differential equations theory. The grant aims to advance machine learning methodology for ubiquitous problems involving sequentially observed data from multiple agents, such as pandemic spread modeling, cooperative robotics applications, and environmental change analysis. Researchers will develop mathematically principled neural network architectures drawing from recent advances in neural ordinary differential equations and graph networks. A key focus is theoretically grounded algorithms to overcome over-smoothing issues in sequential learning on graphs. The project is expected to yield new understanding of bottlenecks in multi-agent sequential learning and algorithms with broad societal applicability. Graduate students will have opportunities to participate in the research.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $100.0k | 6/23/22 |