The Trustees of the University of Pennsylvania received a $143,659 Project Grant award from the National Science Foundation Division of Mathematical Sciences. The grant was awarded on July 1, 2022 under the Mathematical and Physical Sciences program (CFDA 47.049) to support the research project "COLLABORATIVE RESEARCH: FINE-GRAINED STATISTICAL INFERENCE IN HIGH DIMENSION: ACTIONABLE INFORMATION, BIAS REDUCTION, AND OPTIMALITY." The University will conduct collaborative research through...
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 $299,999 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds research into distance-based statistical methods for analyzing complex, high-dimensional data. The Washington University is the primary awardee, with work conducted from Oct. 2021 through Jun. 2024. The University of Pennsylvania serves as a subawardee, contributing to study design, implementation, analysis and manuscripts through the work of Dr. Bhaswar B....
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $250,000 to The Trustees of the University of Pennsylvania to develop advanced statistical methods for integrating and analyzing large-scale data from multiple sources, such as electronic health records and genomics data. The project aims to devise new data-driven algorithms with theoretical optimality guarantees for transfer learning, as well as adversarially...
This $200,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) aims to develop novel feature selection techniques for supervised and unsupervised machine learning models. The research will focus on the "knockoff method" for identifying key predictive features while controlling false discoveries, incorporating microbiome data structures, handling missing values, and...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $125,667 Project Grant to Virginia Polytechnic Institute & State University (Virginia Tech) under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund collaborative research to develop new theories and methodologies for multiple hypothesis testing on regression analysis, which is critical for analyzing high-dimensional data in the era of big data. The research project will create...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of innovative statistical and mathematical methods for time series data analysis. The key objectives of this 2-year project are: a) Developing a variable selection method to identify significant exogenous covariates in autoregressive conditional heteroscedasticity (ARCH) models. b) Designing a novel nonparametric hypothesis test to...
The Trustees of the University of Pennsylvania received a $346,661 Project Grant award from the National Science Foundation under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund analysis of nonlinear partial differential equations in free boundary fluid dynamics, mathematical biology, and kinetic theory from June 2021 through May 2024. As part of advancing the NSF's mission to promote progress in the mathematical and physical sciences, the University will use...
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 three-year, $150,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the Regents of the University of Minnesota. The grant aims to develop new statistical methods for inference with high-dimensional dependent data, such as that generated by single-cell and spatially-resolved sequencing technologies. Specifically, the project will address challenges in statistical inference near the boundary of the...