This National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, titled "COLLABORATIVE RESEARCH: LEARNING AND FORECASTING HIGH-DIMENSIONAL EXTREMES: SPARSITY, CAUSALITY, PRIVACY," aims to develop new statistical methods for forecasting extreme events and assessing their impacts. The $200,000 award to Cornell University, valid from August 15, 2023 to July 31, 2026, seeks to address challenges in analyzing high-dimensional, contaminated data to extract key features...
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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of statistical and machine learning methods to study weather and climate extremes. The $322,555 award, which runs from December 1, 2024 to July 31, 2025, will focus on three key areas: (1) validating climate models in their ability to accurately represent real-world climate extremes, (2) detecting changepoints and estimating break times in...
This Project Grant award of $240,000 from the National Science Foundation's (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), supports research at Columbia University on unsupervised learning and nonlinear dimension reduction. The award aims to advance statistical foundations for analyzing complex, high-dimensional datasets by: (1) developing new empirical Bayes methods to model latent parameter distributions directly from data, and...
This three-year, $260,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will fund research towards designing optimal statistical learning procedures through precise medium-dimensional asymptotic analysis. The grantee, Columbia University, will develop a novel analytical framework to quantitatively characterize the performance of diverse learning algorithms and provide guidance on...
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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $199,961 Project Grant to North Carolina State University to develop a synergistic framework for accurate and real-time prediction of rare extreme events using observational data and mathematical models. The grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), aims to increase the accuracy of extreme event predictions while reducing computational costs to enable real-time...
The National Science Foundation (NSF) awarded a $175,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Arizona State University to develop a geostatistical framework for modeling spatiotemporal extremes, such as heat waves, drought, and intense precipitation. The research aims to better understand the distributions of extreme weather events and develop statistical tools to model the associated spatial and temporal trends and uncertainties. The project will...
This $202,000 Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development of data-driven methods for predicting extreme weather events under climate change. The grant aims to create neural network models capable of capturing the statistics of extreme events, such as heat waves and droughts, in future climate projections. The work builds on previous research on modeling chaotic systems and addressing the spectral bias in neural networks....
Columbia University was awarded a $120,929 project grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund collaborative research on statistical inference methods for high-dimensional spatial-temporal process models from July 1, 2021 to June 30, 2024. As part of the Mathematical and Physical Sciences program's goal of advancing scientific knowledge and understanding of national...