Project Grant 2512644
Award Date
9/1/25
Completion Date
8/31/28
Dollars Obligated
$380K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
East Lansing, MI 48824, USA
Similar Awards
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 $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...
This Project Grant award of $600,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research at the Massachusetts Institute of Technology (MIT) to develop a mathematical foundation for using graph data in machine learning tasks. The goal is to create principled methods for exploiting the latent geometry and structure underlying graph data, such as from social networks or protein interaction networks, to improve machine learning...
The National Science Foundation awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $170,000 to Michigan State University (MSU) for a 3-year project titled "Next-Generation Functional Data Analysis via Machine Learning". The project aims to develop advanced machine learning and artificial intelligence methodologies tailored for the analysis of complex, high-dimensional functional data that are becoming more prevalent across various...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to develop new methods for analyzing, generating, and optimizing graph-structured data. The project aims to create more expressive and efficient graph neural network models, improved generative models for graphs, and apply graph learning techniques to optimization problems and physical systems modeling. The...
This three-year, $300,000 project grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to Michigan State University (MSU) provides USD 113,018 to develop robust machine learning methods for imaging applications. The award aims to advance supervised and unsupervised learning techniques that can effectively reconstruct and correct images from limited or corrupted measurement data, with applications in medical imaging, industrial and security systems,...
The National Science Foundation Division of Mathematical Sciences awarded Michigan State University $249,652 under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to support collaborative research towards designing optimal learning procedures via precise medium-dimensional asymptotic analysis. The three-year project grant aims to develop a novel analytical framework to quantitatively characterize the performance of diverse machine learning algorithms and provide...
The National Science Foundation awarded a $515,999 Project Grant to the University of Notre Dame under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop comprehensive methods for learning to augment graph data through machine learning algorithms. Over a three-year period from March 2022 to February 2025, the University will deliver novel techniques to augment graph data by counterfactual inference on edges as treatment variables, forecasting...