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 statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets. The research focuses on three main areas: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory and methods for...
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...
The National Science Foundation Division of Mathematical Sciences awarded Michigan State University $249,652 under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to support collaborative research towards designing optimal learning procedures via precise medium-dimensional asymptotic analysis. The three-year project grant aims to develop a novel analytical framework to quantitatively characterize the performance of diverse machine learning algorithms and provide...
This $126,025 five-year Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance machine learning techniques through the development of new mathematical tools for analyzing and visualizing complex, high-dimensional data. The key objectives are to create robust manifold learning algorithms that can handle noisy data, preserve local and global geometric details, and effectively cluster collections of manifolds. The...
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...
This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $216,296 to Louisiana State University (LSU) from September 1, 2024 to August 31, 2027. The project aims to develop novel approaches and underlying theory for online machine learning, with a focus on applications in biomedical research, finance, cybersecurity, and big data. Key aspects include: Exploring the use of partial differential equations and optimal...
This Project Grant from the National Science Foundation's Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $399,865 to Michigan State University to develop the MSU Data Machine high-memory compute cluster. The Data Machine addresses the exponential growth of large, complex datasets requiring different computing approaches than traditional high performance computing, through nodes with large memory,...
This National Science Foundation project grant of $239,962 will support the development of novel statistical methods for analyzing functional and imaging data supported on complex geometries through the Mathematical and Physical Sciences program (CFDA 47.049). Led by the University of Washington with a completion date of June 2025, key products include generalized linear models and regularized linear models to predict outcomes from functional predictors on multidimensional non-linear domains....
This Project Grant award, valued at $485,913, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to Michigan State University. The goal of the project is to advance the applicability of machine learning methods through the development of "zeroth-order machine learning" (ZO-ML) techniques. Key deliverables include both foundational research and practical applications of ZO-ML, particularly...
This $290,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), will support the development of data exploration and visualization tools for Fréchet regression analysis of metric-space valued data at Penn State University. The goal is to systematically develop a set of tools to assist with preliminary data exploration, visualization, model diagnostics, and improved estimation...