This $279,399 Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to adapt foundation models for multimodal sequential decision-making applications. The project aims to develop novel techniques that leverage foundation models, which are complex neural networks trained on large datasets, to enhance decision-making capabilities in areas such as smart manufacturing, multi-agent...
This $598,448 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop theoretical and algorithmic foundations for online learning and decision-making involving sequential data under unknown stochastic models. The award focuses on three key areas: (1) representation learning of nonlinear and nonparametric time series models, (2) statistical inference and learning...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research on developing machine learning models of dynamic decision-making. The $300,000 award, effective December 1, 2024 through November 30, 2027, aims to advance methodologies for learning structural models of control behavior. This includes considering diverse data sources, such as demonstrations and preferences, and evaluating the...
This $300,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports collaborative research on the mathematical and algorithmic foundations of multi-task reinforcement learning. The research aims to address the challenge of data efficiency in reinforcement learning, developing new approaches that can learn multiple related tasks simultaneously using less data and computational resources compared to learning...
This Project Grant from the National Science Foundation Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $250,000 to Iowa State University to support research and education activities from March 1, 2022 to February 28, 2025.
The award will fund the development of novel strategies for solving sequential decision-making problems under uncertainty with submodular rewards. Researchers will create...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program totaling $199,299 will support a collaborative research project to develop and evaluate novel AI-based methods for student modeling and tailored large language models to enhance personalized learning in computer science education. The research aims to advance the state-of-the-art in modeling students' problem-solving strategies and algorithmic thinking,...
The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Texas A&M Engineering Experiment Station (Tees) to conduct research on inverse reinforcement learning with heterogeneous data. The research aims to develop machine learning models that can capture an agent's dynamic decision-making behavior, including their preferences and understanding of the environment. This...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to Carnegie Mellon University. The grant, dated September 1, 2024, will fund research to develop advanced foundation models and generative AI capabilities for processing and analyzing large, multimodal spatiotemporal datasets. The research aims to enable new analytical capabilities in areas like urban planning, public safety, and environmental monitoring,...
This National Science Foundation Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $467,141 to the University of Georgia Research Foundation to investigate decision-making frameworks for open multi-agent systems with various forms of uncertainty. The research aims to develop novel planning and reinforcement learning techniques to enable agents to operate optimally in open contexts where the system composition, tasks, and agent...
The National Science Foundation Division of Computing and Communication Foundations awarded $800,000 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to the University of California, Berkeley for a four-year collaborative research project grant. The project aims to improve the sample efficiency of reinforcement learning algorithms in both offline and online settings through techniques like optimistic exploration and pessimistic exploitation. It...
This federal Project Grant award of $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to adapt foundation models, which are complex neural networks trained on large datasets, for multimodal sequential decision-making.
The funded research will develop novel techniques to leverage foundation models in areas such as smart manufacturing, multi-agent systems, and human-machine interaction. Key research thrusts include exploring contextual bandits, multimodal reinforcement learning, and cooperative multi-agent coordination, with the goal of significantly expanding the applicability of foundation models for sequential decision-making tasks. The project will also support educational and outreach activities, including course material development and undergraduate research mentoring. This award, with a performance period from January 2025 to December 2027, reflects NSF's mission to advance scientific discovery and technological innovation.