Project Grant 1800466

This three-year Project Grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $300,000, will support the development of temporal network embedding methods to map time-varying network data to trajectories in a latent space. Specifically, the awardee will establish a family of temporal network embedding techniques that represent the network structure at a given time point succinctly as a point on the trajectory. By embedding entire networks rather than...

This National Science Foundation (NSF) project grant, awarded under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support collaborative research to develop real-time topological data analysis capabilities for high-rate, nonlinear, and nonstationary dynamic systems. The $266,000 project, awarded on May 15, 2023 to the University of South Carolina, aims to integrate topological data analysis with machine learning to improve predictive modeling and...

This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $109,999 to support research at the University of Maryland Baltimore County on novel modeling and Bayesian analysis of high-dimensional time series data. The three-year award beginning September 2022 will fund the development of a framework to represent multi-dimensional time series data as independent stationary latent processes modeled with unspecified spectral densities. The...

This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $180,000 to North Carolina State University from September 1, 2022 through August 31, 2025. The funding supports research to develop novel modeling and Bayesian analysis techniques for high-dimensional time series data. Specifically, the awardees will create a framework to represent multi-dimensional time series data as independent latent time series, allowing for more accurate...

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...

This three-year, $300,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of statistical methods for learning the evolution of connectivity in complex time series data. Key products include estimation and inference methods for a Nonstationary Graphical Model framework called NonSTGM that captures nonstationary dynamics in multivariate systems through a sparse...

The National Science Foundation Division of Mathematical Sciences awarded a $110,000 Project Grant to the University of Florida Division of Sponsored Research under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The three-year award will support the development of novel modeling and Bayesian analysis methods for high-dimensional time series data. Specifically, the principal investigators will create a framework to represent multi-dimensional time series data as a...

The Trustees of Columbia University in the City of New York (Columbia University) was awarded a $329,639 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant funds the development of novel statistical methodology to model, analyze, and predict the evolution of geometric and topological features in random objects over time. Columbia University researchers will establish...

This three-year, $585,744 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at Vanderbilt University to develop novel mathematical techniques and algorithms for designing cost-effective space-time sampling and reconstruction strategies for time-evolving functions on graphs. A diverse team of PhD students, postdoctoral fellows, and senior researchers will study optimal sensor placement for data collection, space-time...

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...