Project Grant 2515158
- This Project Grant award of $111,807 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of robust, low-latency algorithms for detecting changes in non-stationary multi-stream data. The 3-year project, led by the University of Pittsburgh, aims to create algorithms that can quickly and reliably identify changes in the statistical properties of multi-stream data, even with uncertainty about the pre- and post-change data...
- 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 $298,988 federal Project Grant awarded by the National Science Foundation (NSF) under its Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research to develop a novel score-based approach for quickly detecting abrupt changes in the statistical characteristics of online data streams. The project aims to leverage deep neural networks to learn the score (gradient of the log probability density) of data, which can enable change detection without...
- This $152,997 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Southern California (USC) on computer-intensive statistical inference methods for high-dimensional and massive datasets. The project aims to develop efficient, scalable, and statistically robust inferential procedures for two classical problems - change point detection/identification and computationally-aware statistical...
- This $292,362 federal Project Grant award, provided by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program, aims to develop new mathematical theory and statistical tools for monitoring changes in complex systems, such as global trade networks. The project will provide real-time change detection methods with theoretical guarantees for identifying atypical patterns in comprehensive trade databases, with...
- The University of Pittsburgh (Pitt) received a $300,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to conduct research on score-based quickest change detection algorithms for high-dimensional data streams. The project aims to develop fundamental mathematical theories and efficient algorithms for detecting abrupt changes in the statistical characteristics of online data, with applications in anomaly...
- 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...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $249,999 Project Grant to Trustees of Boston University to develop an innovative approach to change-point and anomaly detection using satellite data. The research aims to address challenges in tracking deforestation, degradation, and forest regrowth by leveraging new mathematical frameworks and artificial intelligence techniques. The project will explore applications in areas such as human migration, climate...
- 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 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $193,202 to Columbia University from August 15, 2022 through July 31, 2025. The funding will support research investigating long-time behavior and large population limits of stochastic processes with random times. Specifically, the university will develop mathematical and computational tools to analyze complex interacting systems, with a focus on stochastic models involving...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $175,000 to New York University (NYU) to develop new mathematical, computational, and statistical theories and tools for active sequential change-point detection in high-dimensional streaming data under sampling or resource constraints. The project aims to create computationally scalable and statistically efficient algorithms to detect sparse changes in high-dimensional data, with applications in areas such as biosurveillance, environmental monitoring, and threat detection. The research is expected to advance the state of the art in sequential analysis, change-point detection, and large-scale data inference, while also providing graduate student training. The award period runs from Sep 1, 2025 to Aug 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 7/28/25 |