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...
This $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
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, $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...
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...
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...
Rutgers, The State University was awarded a $199,999 Project Grant from the National Science Foundation Division of Mathematical Sciences on July 1, 2021, with a completion date of June 30, 2024. The grant is funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these fields and strengthen the nation's scientific enterprise. Under this award, Rutgers will conduct research to infer historical network patterns and dynamics using...
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 $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
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...