This Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences, CFDA 47.049 Mathematical and Physical Sciences, provides $225,000 in funding to Carnegie Mellon University from September 1, 2023 to August 31, 2026. The award supports research on feature selection techniques for high-dimensional data analysis in several challenging areas: Expanding a large-scale dataset on statisticians' publications from 1971-2015 to 1971-2025 to enable network analysis...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
This $240,000 Project Grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of computationally efficient algorithms to approximate the impact of removing data subsets from high-dimensional machine learning models. The research aims to advance scientific understanding of artificial intelligence, improve the robustness of decision-making systems, and contribute to the development of privacy-preserving...
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 from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) in the amount of $197,007 is supporting collaborative research to develop new statistical theories and methodologies for tackling issues related to false discovery rate control in regression analysis. The research aims to provide novel statistical tools for analyzing complex data from diverse scientific domains such as brain imaging, genome-wide association studies, and atmospheric...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new theoretical frameworks and computational tools for self-supervised representation learning, with a focus on applying these advancements to biomedical research. The key products and services to be delivered under this 3-year grant include: Investigating self-supervised learning on low-dimensional nonlinear models to capture the...
This $180,000 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of novel community detection tools and frameworks for analyzing weighted network data, with a focus on applications in bioinformatics and biological science. The primary goals are to identify highly correlated gene modules by leveraging covariance or correlation matrix representations, and to provide a systematic, computationally...
This $250,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of algorithms for real-time dynamic risk identification and monitoring of streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The key objectives are to create a unified framework for dynamic risk detection that can be incorporated into...
The National Science Foundation awarded a $150,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Illinois for the period of August 15, 2022 through July 31, 2025. The grant funds research to develop and study optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the principal investigators will first develop and analyze subdata selection for clusterwise linear...
This $149,961 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support research into optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the awardee, George Mason University, will develop and study subdata selection frameworks and methods based on clusterwise linear regression and logistic-normal mixture models. Information-based optimal subdata...