Project Grant 2339682

Award Date 7/1/24
Completion Date 6/30/29
Dollars Obligated $179K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Baltimore, MD 21218, USA
Similar Awards
This $160,118 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program will support The Johns Hopkins University in developing fast and accurate machine learning algorithms with interpretable mechanisms for learning from complex datasets. The project aims to close the theoretical and computational gap between data-independent and data-adaptive random partitioning methods in machine learning, by utilizing and expanding the toolkit of...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $418,035 to Carnegie Mellon University to explore new ways of addressing complex mathematical problems by integrating advanced machine learning techniques with automated reasoning. The project will use three specific open problems within graph theory and combinatorics as test cases to evaluate the effectiveness of new algorithms. Key objectives include applying...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) in the amount of $568,008 will support research into non-canonical representations and symmetries for graph neural networks (GNNs) and their applications. The research activities are divided into three main tasks: (1) designing a non-canonical representation that can express any graph as a combination of intersecting cliques or communities, (2) developing a procedure...
The Johns Hopkins University received a $340,128 Project Grant award from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The award will support research from July 2023 through June 2026 focused on developing new data science approaches and computational models for large-scale shape and image registration analysis. Specifically, the university will conduct theoretical, numerical, and...
The National Science Foundation awarded The Johns Hopkins University a $900,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct collaborative research focused on understanding robustness in machine learning via parsimonious structures from October 1, 2022 to September 30, 2025. Specifically, the University will research conditions under which one can detect adversarial attacks on networks or data poisoning and reconstruct...
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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
This $450,287 Project Grant was awarded by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports research at The Johns Hopkins University to advance the theory of harmonic maps, which have practical applications in areas like medical imaging and computer vision. Specifically, the research focuses on three key areas: (1) developing non-abelian Hodge theory to connect topological data of Kähler...
This $293,784 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports fundamental and applied research on fluctuating systems, random environments, and stochastic algorithms. The research aims to improve understanding and exploitation of randomness across diverse settings, including materials science, fluid dynamics, and machine learning. Key areas of focus include stochastic homogenization, stochastic partial...
This National Science Foundation (NSF) Project Grant award, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $120,000.00 to President and Fellows of Harvard College to develop a novel computational framework for leveraging geometric structure in data. The project aims to advance machine learning methods by incorporating inherent structure in complex data, such as relational networks, hierarchies, and physical symmetries. The research findings will be incorporated into...

This $179,327 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research by The Johns Hopkins University to develop new mathematical and computational techniques that leverage symmetries and differential geometry for machine learning applications in science and engineering.

The key objectives are to improve self-supervised representation learning techniques that enable interpretable modifications to input data, and to develop coordinate-free emulation methods for complex physical systems like cosmology and climate models. The research aims to advance the integration of physics principles into machine learning architectures, with a focus on point cloud and vector field data processing. The project will involve PhD students and high school interns, and includes activities to promote research in Latin America and support women in math and engineering. The award is effective from July 1, 2024 through June 30, 2029.

Generated 5/13/25, 4:14 AM