Project Grant 2152950

Award Date 9/1/22
Completion Date 8/31/25
Dollars Obligated $253K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
College Station, TX 77843, USA
Similar Awards
This $220,000 National Science Foundation project grant supports the development of new statistical inference methodologies for multivariate and functional time series analysis at Texas A&M University from July 2022 through June 2025. The award is funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which supports advancing scientific knowledge and understanding in these fields. Specifically, the university researchers will create a unified framework...
This $177,718 federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program will support research at Southern Methodist University (SMU) to develop new frequency domain methods for analyzing high-dimensional time series data. The project aims to create novel modeling frameworks, dimension reduction techniques, and multi-frequency autoregressive models to enhance the analysis of complex, large-scale datasets in fields...
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...
This $146,738 federal Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences grant program (CFDA 47.049) supports collaborative research on statistical inference methods for multivariate and functional time series data. The primary awardee, The Washington University, will develop a new unified framework using sample splitting and self-normalization techniques to enable robust statistical inference for time...
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 $145,000 Project Grant awarded by the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research on the fundamental theory of wave turbulence. The research at Texas A&M University will tackle challenging problems at the intersection of physics and mathematical analysis, including the rigorous derivation of wave kinetic equations, analysis of the kinetic equation for the Fermi-Pasta-Ulam-Tsingou chain, and well-posedness...
This Project Grant award from the National Science Foundation (NSF) Division of Ocean Sciences under the Geosciences program (CFDA 47.050) provides $323,315 to the University of Texas at Dallas (UTD) to develop a sea-state-dependent numerical algorithm for accurately estimating wind stress over ocean surface waves. The interdisciplinary research team, which includes experts in physical oceanography, air-sea interactions, and atmospheric sciences, will leverage advanced infrared imaging and...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $150,000 Project Grant to The Regents of the University of Colorado (University of Colorado) to develop new statistical methods for analyzing extreme weather events such as heatwaves, droughts, and intense precipitation. The research, conducted under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), aims to better understand the spatiotemporal distributions and trends of these extreme...
This $299,965 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences will allow Colorado State University (CSU) to develop and validate a new statistical model and analytical methods for assessing extremal dependence in high-dimensional data. This work aims to improve quantification of joint risks in applications such as finance, insurance, and climate science. The project will include training a graduate student in extreme value analysis techniques...
This $299,669 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will enable researchers at Texas A&M University to develop flexible Bayesian non-parametric statistical modeling approaches. The primary objectives are to create generative models that allow flexible departures from traditional parametric forms, while retaining the interpretability of parametric Bayesian modeling. This research aims to provide more...

This three-year, $252,816 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Texas A&M University to develop data-driven methods for adaptive frequency band estimation of nonstationary functional time series. The researchers will create computational tools and statistical models to objectively identify optimal frequency bands for summarizing time-varying power spectral information in wind velocity data. This will enhance understanding of storm dynamics and trends over time. The grant also funds professional training programs in atmospheric sciences and statistics through doctoral and undergraduate research supervision as well as outreach to local high school students. The quantitative and standardized frequency band estimation methods developed under this award are expected to improve climate resilience by furthering knowledge of atmospheric conditions and seasonal forecasting.

Generated 1/7/24, 11:20 AM