This Project Grant from the National Science Foundation provides $425,000 to Carnegie Mellon University from July 1, 2021 to June 30, 2024. The funding supports research titled "Statistical Procedures and Performance Measures for Simulator-Based Frequentist Inference" under the Mathematical and Physical Sciences program (CFDA 47.049). The research aims to develop statistical methodologies and performance metrics for frequentist inference approaches that utilize simulation-based...
This National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, awarded to Carnegie Mellon University, provides $240,000 in funding from September 1, 2023 to August 31, 2026. The grant supports research to advance statistical predictive inference methods, addressing challenges in areas like cross-validation, high-dimensional statistical comparisons, and conformal prediction. The project aims to develop novel techniques with strong mathematical justifications that can...
This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
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...
This Project Grant award of $250,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports research by Carnegie Mellon University to develop flexible, valid inference procedures for complex modern data that leverage black-box machine learning algorithms. The project aims to advance the foundations of cross-validation techniques to enable adaptive inference in conjunction with powerful black-box models, with potential applications in...
This $375,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at the Massachusetts Institute of Technology (MIT) to advance the understanding of statistical analysis within a broader environment with multiple stakeholders. The project aims to develop statistical protocols that are robust to the behavior of different stakeholders who may have different aims, in order to enable more reliable...
Carnegie Mellon University was awarded a $250,000 Project Grant from the National Science Foundation Division of Mathematical Sciences on July 1, 2021 to complete work by June 30, 2024. The grant supports research under the Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these fields and strengthen the nation's scientific enterprise through increasing knowledge and enhancing understanding of major problems. Specifically, the university will conduct...
This $200,000 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of new statistical methods that incorporate qualitative constraints into semi-parametric models. The awardee, Carnegie Mellon University, will work to create general non-parametric regression estimators that account for subject matter constraints and adapt to the smoothness of the underlying data. Researchers will also explore approaches for...
This Project Grant award, valued at $227,984, was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to the Massachusetts Institute of Technology (MIT). The award supports fundamental and applied research to advance the theoretical and practical understanding of equilibrium computation and learning dynamics in multiagent interactions (games). Key focus areas include: 1) refining algorithms for learning...
This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems supports research to significantly increase the scalability of algorithms for solving large-scale, multi-step, imperfect-information strategic interactions. Specifically, the $854,896 award to Carnegie Mellon University (CMU) from August 1, 2023 to July 31, 2026 will fund the design, implementation, and testing of novel techniques in three main areas: Scalable subtree solving...