Project Grant 2402234

Award Date 10/1/24
Completion Date 9/30/27
Dollars Obligated $160K
Awarding Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Baltimore, MD 21218, USA
Similar Awards
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 federal Project Grant award in the amount of $568,008 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The funding supports research to develop new approaches for representing and processing graph-structured data, such as social networks or molecular structures, using novel "non-canonical" graph representations and corresponding graph neural network models. The key research activities include:...
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...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $179,327 to The Johns Hopkins University for a 5-year research project titled "CAREER: Symmetries and Classical Physics in Machine Learning for Science and Engineering." The project aims to develop new mathematical and computational techniques to further exploit symmetries and differential geometry in the design of machine learning models,...
The National Science Foundation Division of Computing and Communication Foundations awarded a $219,202 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to The Johns Hopkins University. The five-year award beginning July 1, 2023 will support the development of formal methods and algorithms to increase the interpretability and robustness of machine learning models. Specifically, the awardee will define new notions of local feature...
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 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 $379,999 Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences is funding the development of novel graph-based semi-supervised machine learning techniques that can effectively learn from limited labeled data. The project aims to address the challenge of scarce labeled data in many machine learning applications by incorporating a graph-based semi-supervised learning framework. Specifically, the research encompasses three key objectives: (1)...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will enable the development of new statistical machine learning approaches and theory termed "minipatch learning." The goal is to enable faster computation and improved statistical efficiency for uncovering insights from massive, complex datasets commonly found in fields like biomedicine, genomics, and neuroscience. The award of $195,479 to The Trustees of...
This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...

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 random tessellation processes in stochastic geometry. The goals are to develop state-of-the-art random partitioning algorithms, provide matching theoretical performance guarantees, and study fundamental statistical and computational trade-offs of data-adaptivity in the partitioning process. This work is intended to produce interpretable and theoretically justified algorithms that will be valuable for safety-critical applications in engineering and healthcare. The award period runs from October 1, 2024 through September 30, 2027, and no subawards are planned.

Generated 5/13/25, 5:48 AM