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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $100,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Rutgers, The State University located in New Brunswick, New Jersey. The grant supports the development of advanced statistical models and software to predict and assess the likelihood of extreme geopolitical events with quantified uncertainty. The project aims to construct a comprehensive, data-driven prediction...
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...
The National Science Foundation (NSF) awarded a 3-year, $250,015 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The primary goal of this collaborative research project is to develop advanced statistical and machine learning techniques to forecast and analyze high-dimensional extreme events, such as extreme climate conditions and social phenomena. Key research thrusts include: 1) Learning the...
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 $220,000 Project Grant to the International Computer Science Institute (ICSI), a non-profit research organization, under the Mathematical and Physical Sciences program (CFDA 47.049). The project aims to develop resilient and reliable deep learning methods for forecasting complex spatiotemporal ground motion data, with applications in seismology, earth sciences, and other domains. Key technical objectives include...
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 three-year, $260,000 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at Duke University on large deviations and extremes for random matrices, tensors, and fields. The grant aims to advance understanding of rare events and extreme values through analysis of nonlinear functions of random hypergraphs and matrices, with a focus on applications to social networks and reaction-diffusion systems modeling invasive...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $322,555 to The Washington University in St. Louis to develop statistical and machine learning methods for studying weather and climate extremes. The three-year project will focus on three key areas: (1) validating climate models' ability to mimic real climate extremes, (2) detecting changepoints and estimating break times in extreme weather and...