Project Grant 2210726

Award Date 7/1/22
Completion Date 6/30/25
Dollars Obligated $300K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
College Station, TX 77843, USA
Similar Awards
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 modeling nonstationary time series data. Specifically, the awardee Cornell University will develop estimation and inference methods for a nonstationary graphical model framework called Nonstationary Graphical Models (NonstGM). This framework captures nonstationary...
This Project Grant award of $146,738 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative statistical research on multivariate and functional time series analysis. The research will develop new nonparametric inference procedures that can accommodate a wide range of data dimensionality and require weak assumptions on the data generating processes. The methodology will be disseminated through publications, presentations, and...
This $300,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) will fund research into spectral methods for single and multiple graph inference networks. The grantee, North Carolina State University, will develop efficient parameter estimation methods for latent position graphs and valid two-sample testing procedures for comparing latent position graphs while ignoring irrelevant features. The...
This National Science Foundation (NSF) Project Grant award, under the STEM Education (CFDA 47.076) program, provides $150,000 in funding to Lehigh University for a collaborative research project titled "DYNAMIC BRAIN GRAPH MINING - MAPPING THE CONNECTIONS IN HUMAN BRAINS AS NETWORKED SYSTEMS." The project aims to develop new methods for modeling the dynamics of brain graphs derived from neuroimaging data, in order to generate accurate, interpretable, and fair predictions about...
This $365,274 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of novel mathematical techniques and algorithms for designing cost-effective space-time sampling strategies and reconstruction methods for time-evolving functions on graphs. A diverse group of researchers from Northern Illinois University will work to analyze and manage various time-evolving processes sampled under realistic conditions and...
This Project Grant award of $174,344 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative statistical modeling and inference research for object-valued time series data. The research aims to develop new models, methodology, and theory for analyzing data representing random objects in metric spaces, such as intraday financial asset returns, age-at-death distributions, energy source compositions, and medical imaging data....
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $225,444 to the Research Foundation of the City University of New York (RFCUNY) - Baruch College to develop advanced statistical methods for analyzing physiological signals. The project aims to address challenges researchers face in fully utilizing the information contained in physiological data collected under modern study designs. Specifically, the research...
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...
This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
This $240,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at the University of California, Davis (UC Davis) to develop a comprehensive framework for analyzing nonlinear and non-Gaussian functional time series data. The research aims to produce new probabilistic results, introduce two novel functional time series models, and develop accompanying inference procedures for statistical analysis. This...

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 operator in the Fourier domain. Algorithms will be developed and validated on real electroencephalogram data to model local and periodic nonstationarity, allowing for abrupt changes and smooth evolution of temporal dynamics. Graphs with time-varying vector autoregressive models will provide insights into complex systems. All methods and software will be made publicly available. Outcomes are expected to aid clinical researchers' understanding of connectome abnormalities. The grant also supports training graduate students and enhancing diversity in statistical sciences.

Generated 1/6/24, 9:15 PM