Project Grant 2311304
Award Date
8/1/23
Completion Date
7/31/26
Dollars Obligated
$100K
Funding Federal Agency
Office of Integrative Activities
Awarding Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.083
Assistance Type
Project Grant
Place of Performance
Piscataway, NJ 08854, USA
Similar Awards
Rutgers, The State University has been awarded a $300,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to conduct research titled "UNRAVEL MACHINE LEARNING BLACKBOXES -- A GENERAL, EFFECTIVE AND PERFORMANCE-GUARANTEED STATISTICAL FRAMEWORK FOR COMPLEX AND IRREGULAR INFERENCE PROBLEMS IN DATA SCIENCE." Over a three-year period from July 1, 2023 to June 30, 2026, Rutgers will develop novel statistical and computational...
This $128,238 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant is funding research at the University of Texas at Austin to develop a unified, principled reinforcement learning (RL) framework that can efficiently solve large-scale, real-world sequential decision-making problems. The key focus areas include advancing off-policy RL algorithms, exploring connections between...
This $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
The National Science Foundation awarded a $600,000 Project Grant to Princeton University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research toward developing a mathematical foundation for deep reinforcement learning over the period from October 1, 2022 to September 30, 2026. Specifically, the project aims to bridge current gaps in theoretical reinforcement learning and deep neural networks by investigating guarantees achievable by...
Rutgers, The State University was awarded a $749,708 project grant from the National Science Foundation under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will fund the "FMITF: TRACK I: SYNTHESIS AND VERIFICATION FOR PROGRAMMATIC REINFORCEMENT LEARNING" project from October 1, 2021 through September 30, 2025. The project aims to advance the development and use of research cyberinfrastructure to enable and accelerate discovery and innovation in...
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...
This federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $193,000 to the Trustees of Princeton University over a 3-year period starting September 1, 2024. The funding supports collaborative research on developing safe reinforcement learning techniques that can be applied in domains like robotics, autonomous driving, and power systems. The key research thrusts include: 1) training robust policies using distributionally robust approaches;...
The National Science Foundation (NSF) awarded a $237,438 Project Grant to Purdue University under the Social, Behavioral, and Economic Sciences grant program (CFDA 47.075) to advance statistical inference on dynamic systems. The 3-year project will leverage deep learning and statistical modeling to enhance the efficiency, accuracy, and interpretability of time-series analysis across various domains. The research will introduce a new neural inference framework for estimating and inferring dynamic...
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 $600,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will support research at Stanford University toward developing a mathematical foundation for deep reinforcement learning. Over four years, the grant will fund three research thrusts investigating the types of guarantees achievable by reinforcement learning policies under different problem structures and increasing neural network complexity. The researchers will also...