The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $100,000 Project Grant to the University of Central Florida (UCF) to develop efficient and effective algorithms for detecting anomalies in high-dimensional spatiotemporal data with large amounts of missing data. Under CFDA 47.049 - Mathematical and Physical Sciences, the project aims to address the challenge of predicting rare anomalies using high-dimensional real-world data with mixed-type multivariate response,...
This National Science Foundation Project Grant of $199,999 will support the development of modern spatial and shape analysis methods for heterogeneous high-dimensional geospatial data. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the award will be carried out from July 2022 through June 2025 by the University of Texas at Dallas. Specifically, the Principal Investigator will create three modeling frameworks to analyze heterogeneous geospatial data at different...
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...
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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $100,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Rutgers, The State University located in New Brunswick, New Jersey. The grant supports the development of advanced statistical models and software to predict and assess the likelihood of extreme geopolitical events with quantified uncertainty. The project aims to construct a comprehensive, data-driven prediction...
This $180,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences supports the development of a multimodal transformer-based model for time-series prediction and spatiotemporal analysis. The project aims to create algorithms for forecasting time-series, predicting spatial dynamics, and detecting anomalies, which can be applied to data analysis and high-consequence decision-making. The research will address how to utilize contextual information with...
The National Science Foundation (NSF) awarded a $399,999 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Texas at Dallas (UTD). The funding supports a collaborative research project to develop and harness quasi-one-dimensional topological materials for novel electronic, optoelectronic, and sensing functionalities. The project aims to overcome key challenges in current topological insulator materials by focusing on quasi-one-dimensional...
This $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $359,940, will support the development of new statistical models and algorithms for analyzing large, spatially-dependent data sets collected from complex domains with irregular boundaries. Specifically, the awardee, Texas A&M University, will introduce a class of nonstationary models that can flexibly characterize potentially heterogeneous spatial dependence while...