Project Grant 2532852
- This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $225,444 to the Research Foundation of the City University of New York (RFCUNY) - Baruch College to develop advanced statistical methods for analyzing physiological signals. The project aims to address challenges researchers face in fully utilizing the information contained in physiological data collected under modern study designs. Specifically, the research...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Office of International Science and Engineering (CFDA 47.079) supports collaborative research to develop robust statistical methods for analyzing high-dimensional, nonstationary time series data. The research aims to construct reliable estimators of autocovariance structures that can accommodate outliers and structural changes, enabling more accurate detection and quantification of shifts in complex, evolving systems....
- 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) is funding collaborative statistical research and methodology development for analyzing object-valued time series data. The $174,344 award to The Washington University, which began on January 1, 2025, will support the development of new models, techniques, and theory for statistical inference and change detection in object-valued time series across various scientific and...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of innovative statistical and mathematical methods for time series data analysis. The key objectives of this 2-year project are: a) Developing a variable selection method to identify significant exogenous covariates in autoregressive conditional heteroscedasticity (ARCH) models. b) Designing a novel nonparametric hypothesis test to...
- This federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) focuses on developing new frequency domain modeling techniques for analyzing high-dimensional time series data. The $177,718 award to Southern Methodist University (SMU) will fund research to create a new modeling framework that enables dimension reduction and correlation analysis of large, complex time series datasets across disciplines such as neuroscience,...
- 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 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $262,000.00 to the Research Foundation of the City University of New York (RFCUNY) to conduct collaborative research on "Spectral Analysis of Limiting Operators in Higher Dimensions". The project aims to develop new mathematical techniques that enhance signal analysis by precisely isolating distinct signal components, leading to improvements in...
- This National Science Foundation (NSF) Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), provides $25,000.00 to The Pennsylvania State University to conduct collaborative research on developing new statistical methods for analyzing high-dimensional, nonstationary time series data. The research aims to construct robust estimators of autocovariance structures that can accurately handle outliers and large deviations, as well as develop efficient procedures...
This Project Grant award of $246,755.00 from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) to the Research Foundation of the City University of New York (RFCUNY) will develop advanced statistical tools capable of analyzing complex modern time series data. The goal is to uncover hidden signals in vast volumes of data from fields such as biomedicine, economics, and finance that could lead to better medical diagnoses, inform policy decisions, and enhance market stability. The research will establish robust frequency detection and signal processing methods built upon novel spectral dependence metrics that comprehensively capture both linear and nonlinear temporal dependence in functional, tensor, and high-dimensional data forms. This work directly supports NSF's mission to advance national health, prosperity, and welfare through scientific progress, while also providing new computational tools to benefit researchers across multiple disciplines and enhance educational opportunities in data science and statistical modeling.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $246.8k | 7/29/25 |