This National Science Foundation project grant of $167,505 will fund research on data assimilation techniques for turbulent fluid flows from July 2022 to June 2025 at the University of Nebraska-Lincoln. Under the Mathematical and Physical Sciences program (CFDA 47.049), the grant supports the development and testing of new algorithms to incorporate observational data into mathematical models of complex multi-scale phenomena like weather, ocean dynamics, and groundwater flow. Specifically, the...
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 $185,163 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of scalable Gaussian process methods for spatial statistics and machine learning. The project aims to create a universal toolbox for highly accurate and computationally efficient Gaussian process modeling to enable improved data analysis, prediction, and uncertainty quantification across diverse applications like carbon monitoring,...
This three-year National Science Foundation project grant of $174,703 supports research at Brigham Young University to develop new data assimilation techniques for turbulent fluid flow modeling and prediction. The award is made through the Mathematical and Physical Sciences program, which aims to strengthen the nation's scientific enterprise through increased mathematical and physical sciences knowledge and understanding of major national challenges. Specifically, the university researchers will...
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...
This $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $175,000 to the University of Georgia Research Foundation, Inc. to initiate a new paradigm for statistical inference of high-dimensional time series data. The project aims to develop self-normalized inference methods that can quantify the accumulative uncertainty of high-dimensional data collected over time, which has been a challenge with existing techniques....
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 $750,800 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at the University of Colorado Denver to advance data-consistent inversion methodology for quantifying uncertainties in coastal hazard modeling. The grantee aims to develop a deep learning-based data-to-distribution pipeline to transform spatial-temporal data into non-parametric distributions for data-consistent inversion. This will incorporate...