Project Grant 2313131
Award Date
8/1/23
Completion Date
7/31/26
Dollars Obligated
$300K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Chicago, IL 60637, USA
Similar Awards
This National Science Foundation (NSF) Division of Information and Intelligent Systems Project Grant aims to advance "few-round active learning" algorithms and enable more efficient training of supervised machine learning models. The $300,000 award to Virginia Polytechnic Institute & State University (Virginia Tech), running from August 1, 2023 to July 31, 2026, will support research to: 1) develop methods for quantifying the utility of unlabeled data for active learning tasks, and...
This $299,999 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to enhance the performance of reinforcement learning (RL) systems in completing complex tasks in challenging environments. The project aims to develop new task and environment representations to enable active learning strategies that optimize resource allocation and reduce the need for extensive physical interactions with the...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $599,963 to the University of Southern California (USC) will support research to develop a new mathematical lens for understanding machine learning, focused on combinatorics, optimization, and graph theory. The project aims to unlock answers to fundamental questions in machine learning, such as what is learnable and the characteristics of problems that...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
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 Project Grant award of $236,099 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop robust algorithms for machine learning and optimization under dynamic and uncertain data conditions. The research aims to address limitations of existing approaches by leveraging the structure of fundamental supervised learning tasks like regression and classification. The research will advance techniques in...
The National Science Foundation Division of Information and Intelligent Systems awarded a $1.2 million Project Grant to the International Computer Science Institute of Berkeley, California from October 1, 2021 to September 30, 2025. The grant supports research under the Computer and Information Science and Engineering program (CFDA 47.070) to develop scalable second-order methods for training, designing, and deploying machine learning models. The CFDA program aims to advance computing and...
The National Science Foundation awarded $295,563 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to the University of Rochester. This two-year Project Grant will support the development of a novel programming-free and visual-based machine learning environment to enable high school students and teachers with limited data skills to discover complex scientific phenomena through pattern analysis of real-world data. Key activities include creating the...
This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits of...
The National Science Foundation (NSF) awarded a $1,182,881 Project Grant to the University of California, San Diego (UCSD) under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of new methods to ensure that machine learning models assigned to decisions such as lending and hiring can be changed through individual actions, protecting the right to access these services. The project will create techniques for (1) detecting...