Project Grant 2316428
- This National Science Foundation (NSF) Project Grant award of $384,214 to Northeastern University will develop statistical inference methods that leverage advanced techniques like reinforcement learning, Bayesian statistics, and machine learning. The project aims to 1) incorporate expert knowledge into the modeling process without requiring expert oversight, and 2) systematize data collection for accurate inference of complex systems and processes. The proposed approaches will be applied in...
- The National Science Foundation (NSF) awarded a $100,000 Project Grant under its Integrative Activities program (CFDA 47.083) to Rutgers, The State University located in Piscataway, New Jersey. The three-year grant, awarded on August 1, 2023, will fund research to develop novel statistical inference tools and computationally efficient approaches for reinforcement learning in high-dimensional, non-identically distributed data settings. Key focus areas include statistical inference for...
- This Project Grant award of $375,000.00 from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research conducted by Purdue University. The research aims to develop statistical methods and computational tools for analyzing relationships between variables in psychometric studies, represented as graphical models. Key products of this project include: Development of scalable, likelihood-based inference methods for intractable exponential...
- This $375,000 Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to advance Bayesian inference methods for the analysis of complex human data. The project, conducted by Rensselaer Polytechnic Institute, will develop an efficient amortized Bayesian inference framework that enables researchers across the social and behavioral sciences to quickly fit, criticize, and adapt complex computational models. The...
- This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...
- This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
- The National Science Foundation awarded a $249,999 Project Grant to North Carolina State University on May 15, 2021 under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). The grant supports research on imprecise probability and valid statistical inference through April 30, 2024. As part of this effort, Rutgers, The State University will receive a sub-award to contribute to the project. The overarching goal of the NSF program is to promote basic research and education in the...
- This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
- This Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to Carnegie Mellon University to develop a methodology for simulation-based inference that uses random features rather than carefully designed summary statistics. The 3-year grant, which runs from August 15, 2023 to July 31, 2026, aims to create a practical and generic tool for fitting simulation models to real-world data across diverse domains like astronomy, ecology, climate science, and...
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 systems, which differs from conventional likelihood-based or Bayesian approaches. The results will be disseminated through academic publications, conferences, and open-source software to benefit researchers and practitioners. The project will also involve graduate students and high school students in the research process.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $237.4k | 7/25/23 |