This National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, under the Mathematical and Physical Sciences program (CFDA 47.049), awarded Colorado State University $144,726 to develop and apply new algebraic tools to enhance statistical and AI methods for detecting outliers, recovering missing data, and identifying hidden constraints in high-dimensional data analysis. The research aims to create a self-adaptive, linear-time algorithm to separate signals, find hidden...
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...
The National Science Foundation (NSF) awarded a $100,000 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to the University of Texas at Dallas (UTD) for the project "Predictive Anomaly Detection for Spatio-Temporal Data with Multidimensional Persistence". The project aims to develop novel machine learning and topological data analysis techniques to model spatial and temporal interdependencies in large spatio-temporal datasets, with applications in...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $249,999 Project Grant to Trustees of Boston University to develop an innovative approach to change-point and anomaly detection using satellite data. The research aims to address challenges in tracking deforestation, degradation, and forest regrowth by leveraging new mathematical frameworks and artificial intelligence techniques. The project will explore applications in areas such as human migration, climate...
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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $199,961 Project Grant to North Carolina State University to develop a synergistic framework for accurate and real-time prediction of rare extreme events using observational data and mathematical models. The grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), aims to increase the accuracy of extreme event predictions while reducing computational costs to enable real-time...
This $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
This Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) provides $114,416 to Bucknell University to conduct collaborative research on detecting and combating threats embedded within communications streams and online forums. The research aims to develop advanced statistical and artificial intelligence methods to rapidly recover data structures and detect outliers in multidimensional...
The National Science Foundation (NSF) awarded a $200,000 Project Grant to Iowa State University of Science and Technology to develop new algorithms and a toolkit for analyzing spatiotemporal datasets to better understand human behavior and mobility patterns. The 3-year project, funded under the NSF Mathematical and Physical Sciences program (CFDA 47.049), aims to combine topological data analysis and time-frequency analysis to detect anomalies in datasets like volumetric traffic data and U.S....
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $100,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Georgia Tech Research Corporation (Georgia Tech) from August 15, 2023 to July 31, 2026. The grant will support the development of a "Novel Distributed, Multi-Channel, Topology-Aware Online Monitoring Framework of Massive Spatiotemporal Data" called A-DMIT. This framework aims to advance online threat detection...