Project Grant 2143176
Award Date
8/15/22
Completion Date
7/31/27
Dollars Obligated
$600K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Stanford, CA 94305, USA
Similar Awards
Carnegie Mellon University was awarded a $738,927 project grant from the National Science Foundation Division of Behavioral and Cognitive Sciences under the Social, Behavioral, and Economic Sciences federal grant program (CFDA 47.075). The three-year project grant, awarded September 1, 2021 and set to be completed by August 31, 2024, will support research toward developing personalized machine learning models, reasoning approaches under data-knowledge symbiosis, and interpretable interaction...
This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $316,000 in funding to The Leland Stanford Junior University (Stanford University) to develop a general foundation model framework for scientific discovery using graph-structured data. The research aims to address key limitations in existing graph foundation models, such as the inability to handle complex graph structures or...
The National Science Foundation's Division of Information and Intelligent Systems awarded $454,115 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to Stanford University. The three-year Project Grant will support research to accelerate machine learning through the development of automated methods to clean data, reduce feature dimensionality, and recommend machine learning models using low-dimensional latent vectors. Principal Investigator will...
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 $329,937 Project Grant was awarded by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program to Yale University. The grant supports a computational research project aimed at understanding how humans learn and infer social relationships and group dynamics by observing patterns of interaction between people. The project involves developing a theory and computational model to predict how people make these types of social inferences, as well as...
This $272,750 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports fundamental research at the Massachusetts Institute of Technology (MIT) on how people make decisions and interact based on information. The key research focuses include: 1) understanding the long-term implications of people's limited or imperfect memory, which can lead to overconfidence and neglect of rare events; 2) examining how people decide...
This National Science Foundation Project Grant award of $303,891 supports research at Arizona State University to advance machine learning for human-in-the-loop cyber-physical systems. Under the Computer and Information Science and Engineering program, the award funds development of mixed-initiative solutions to enable accurate learning of human behaviors in uncontrolled environments through mobile and wearable devices. Key objectives include investigating combinatorial approaches to maximize...
The National Science Foundation Division of Information and Intelligent Systems awarded a $154,231 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The two-year award will support research to develop a generative deep learning framework for approximating human decision-making processes on social networks when structural network data is unavailable. Specifically, the university researchers will...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $400,000 to The Leland Stanford Junior University to research methods for encoding societal values into social media ranking algorithms. The key goals are to: 1) develop techniques for translating social science constructs into algorithmic objective functions, 2) create a library of societal objective functions based on validated theory, and...
This Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $300,000 to support research and education activities at Stanford University from October 1, 2022 to September 30, 2025. The award will fund the development of new algorithms for decision tree learning and the establishment of rigorous mathematical guarantees for existing algorithms widely used in...