Project Grant FA23862214039

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 Project Grant award of $113,834 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research at North Carolina State University (NC State) to develop efficient algorithms for optimal transport in geometric settings. The project aims to advance the theoretical foundations of optimal transport and bridge the gap between theory and practice of optimal transport algorithms. By leveraging combinatorial, geometric and...

This three-year Project Grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $399,998, will fund research at Cornell University's Office of Sponsored Programs on statistical optimal transport in high dimensional mixtures. Specifically, the researchers will define a new "sketched Wasserstein distance" measure to compare and analyze high-dimensional datasets arising in fields such as linguistics, computational biology, and particle physics....

This National Science Foundation Project Grant of $315,519 supports research at the University of California, Los Angeles from July 2022 to June 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The award funds research investigating challenging problems in optimal transport theory and its applications in fields including partial differential equations, geometry, probability, and machine learning. Key areas of focus include developing the theory to analyze games with large...

This $400,000 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models. The key objectives are to: 1) create new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2) explore model...

This Project Grant award for $450,000 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will fund research at Texas A&M Engineering Experiment Station (Tees) to investigate the use of machine learning techniques for joint compression and encoding of data sources like speech, images, and video for transmission over noisy communication networks. The key goals are to improve the understanding of the underlying mechanisms of machine learning-based joint...

This Project Grant award of $660,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to advance multi-stream architectures for computer vision and other multi-modal artificial intelligence applications. The project aims to: (1) develop a suite of high-performance multi-stream foundation models for tasks like object detection, text-based image segmentation, and audio-video analysis; (2) create optimized algorithms to...

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 National Science Foundation Project Grant award of $540,000 provides funding from July 1, 2022 to June 30, 2025 to address new challenges in statistical inference with regularized optimal transport. The award is made under the Mathematical and Physical Sciences program (CFDA 47.049) to Cornell University to explore modern regularization techniques for optimal transport distances and develop a comprehensive statistical theory to facilitate principled inference in high dimensions....

This Project Grant award from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), provides $493,505.49 to the University of California, San Diego (UCSD) over a project period from September 12, 2024 to August 31, 2027. The purpose of this award is to use deep neural networks to understand how children's everyday visual experiences interact with statistical learning...