Project Grant 2212032
Award Date
10/1/22
Completion Date
9/30/26
Dollars Obligated
$1.2M
Funding Federal Agency
National Science Foundation
Awarding Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
East Lansing, MI 48824, USA
Similar Awards
This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
This $600,000 National Science Foundation project grant supports research at Stony Brook University to develop a suite of novel distributed reinforcement learning algorithms. The grant is funded through the NSF's Computer and Information Science and Engineering program. Specifically, the three-year award will fund research to establish theoretical foundations for designing, analyzing, and applying fully distributed reinforcement learning algorithms over large-scale networks without global...
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 $175,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel "United Learning" framework. The project aims to efficiently integrate low-resource computing devices, such as personal computers, smartphones, and IoT devices, into the training of complex deep learning models. The key products and services to be delivered include: Knowledge Expansion:...
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...
The National Science Foundation (NSF) awarded a $599,410 Project Grant to Carnegie Mellon University (CMU) to develop a collaborative learning framework for dynamic and diverse computing environments, particularly focused on edge devices. The grant, under NSF's Computer and Information Science and Engineering program (CFDA 47.070), aims to innovate model-parallel collaborative learning by designing unique model architectures and efficient algorithms, facilitate practical on-device training and...
This Project Grant award, valued at $485,913, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to Michigan State University. The goal of the project is to advance the applicability of machine learning methods through the development of "zeroth-order machine learning" (ZO-ML) techniques. Key deliverables include both foundational research and practical applications of ZO-ML, particularly...
This five-year, $199,995 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a co-designed framework of hardware, software, and algorithms enabling extreme-scale machine learning systems for emerging artificial intelligence of things and internet of senses technologies. Specifically, the Saint Louis University team will pursue five research thrusts: developing hardware and compiler approaches for large-scale split learning...
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...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at North Carolina State University to explore advanced sampling and optimization techniques for decentralized machine learning. The key objectives are to: Enhance the sampling efficiency of interacting nonlinear Markov chains through adaptive spatio-temporal repellency among multiple "self-repellent random walks",...