This $600,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund the development of new statistical methods and machine learning frameworks for measuring differences between probability distributions. The grant supports the establishment of conditional transport as a novel statistical distance metric to address limitations of existing distribution comparison methods. It will also develop an efficient...
This $600,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports fundamental and applied research to develop mathematical foundations for leveraging graph data in machine learning tasks. The key objectives are to: Characterize how the geometry of the underlying latent space affects structural and combinatorial properties of graphs, Derive optimal algorithms for recovering latent feature vectors from...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
This $400,000 Project Grant was awarded on July 15, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award will fund a collaborative research project at the University of Michigan focused on developing a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives of the project are: 1) Designing "white-box" deep neural network architectures using...
This $298,450 National Science Foundation project grant supports research to quantify the error landscape of deep neural networks. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the awardee New York University will employ statistical mechanics methods to characterize the basins of attraction in high-dimensional parameter spaces of deep learning models. The university will measure basin volume distributions and flatness as a function of network parameters...
This $600,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to Vanderbilt University on February 1, 2025. The grant will fund a collaborative research project to accelerate the design of controllers for large-scale engineering systems, focusing on the application of artificial intelligence in transportation. The project aims to bridge the gap between simulated cyber environments and real-world...
This Project Grant award for $599,963.00, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support research to develop a new mathematical lens for understanding machine learning through combinatorics, optimization, and graph theory. The principal investigator will explore fundamental questions about machine learning, such as what is learnable and what are the characteristics of problems that enable or prohibit...
This National Science Foundation project grant of $600,000 supports research establishing conditional transport as a new statistical distance measure between probability distributions to address limitations of existing methods. Funded under the Computer and Information Science and Engineering program, the award to the University of Texas at Austin from October 2022 to September 2026 will develop a new distribution-based machine learning framework and efficient approximation algorithms. Key...
This $600,000 project grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to advance artificial intelligence (AI) capabilities by investigating the mathematical foundations and practical applications of deep learning models. The project, awarded to the University of Wisconsin System's University of Wisconsin-Madison campus, will focus on understanding the properties of neural networks trained on...
This Project Grant award of $600,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at the Massachusetts Institute of Technology (MIT) from October 1, 2024 to September 30, 2027. The project aims to design better algorithms for learning problems in linear dynamical systems, graphical models, and hidden Markov models, with the goal of bridging the gap between the tools and perspectives of classic...
This $107,958 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program supports research by Vanderbilt University to advance machine learning methods through the development of novel geometric distance metrics and embeddings. The 5-year project aims to create more efficient, robust, and uncertainty-aware machine learning algorithms with potential benefits in healthcare, transportation, and national defense.
The key research activities include: 1) Developing scalable optimal transport-based metrics that extend beyond probability measures, 2) Creating Euclidean embeddings for the proposed metrics to enable integration with traditional machine learning processes, and 3) Combining the transport-based embeddings with geometric deep learning models to assess their impact on the performance and robustness of these methods. The project will also integrate the research with education and outreach initiatives to engage students from high school to graduate levels, with a particular focus on underrepresented groups.