Project Grant 2334897
- 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 $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...
- The University of Pittsburgh will use $235,948 in funding from the National Science Foundation's Mathematical and Physical Sciences program to support the project "New Frontiers of Robust Statistics in the Era of Big Data" from July 2021 through June 2024. Through this Project Grant award, the University will advance understanding and methodology in robust statistics, an area of mathematics focused on developing statistical techniques that are not overly impacted by outliers or...
- The University of Pittsburgh received a $424,546 project grant award from the National Science Foundation Division of Mathematical Sciences on July 15, 2021 to support work on the TIME ACCURATE PREDICTION OF FLUID MOTION project through June 30, 2024. The grant is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these scientific fields and strengthen the nation's scientific enterprise through increasing knowledge and enhancing...
- The University of Pittsburgh received a three-year, $1.2 million Project Grant from the National Science Foundation under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will fund the acquisition of new GPU and MPI nodes to enhance the Interdisciplinary Pitt Center for Research Computing. The Center supports investigator-initiated research and education in all areas of computing, communications, and information science and engineering. The new...
- Federal Grant Award Summary The University of Pittsburgh received a $328,712 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), effective July 15, 2025 through June 30, 2028. This award supports fundamental research in geometric function theory, focusing on the mathematical analysis of mappings and functions with limited differentiability, including convex functions, Sobolev functions, and...
- The University of Pittsburgh was awarded a $844,205 project grant from the National Science Foundation under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research titled "COLLABORATIVE RESEARCH: NCS-FR: VOLITIONAL CONTROL OF INTERNAL COGNITIVE STATES" from September 2021 through August 2026. The research aims to advance the development and use of cyberinfrastructure to accelerate discovery and innovation in computing,...
- This $299,998 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research at Carnegie Mellon University to develop new big data algorithms that are robust to adversarial input. The key focus areas include: 1) adversarial robustness in black-box and white-box streaming settings, and 2) adaptive data analysis with bounded space. The research team will also explore emerging attack...
- The University of Pittsburgh received a $316,000 Project Grant award from the National Science Foundation Division of Computer and Network Systems to support research titled "COLLABORATIVE RESEARCH: CNS CORE: SMALL: TOWARDS UNSUPERVISED LEARNING ON RESOURCE CONSTRAINED EDGE DEVICES WITH NOVEL STATISTICAL CONTRASTIVE LEARNING SCHEME." The three-year award, issued on October 1, 2021 with a completion date of September 30, 2024, will fund research into unsupervised learning techniques...
- Federal Project Grant Award Summary The University of Pittsburgh received a $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences (CFDA 47.049) awarded August 1, 2025, with a completion date of July 31, 2028. This research project develops novel mathematical methods and analytical techniques to analyze nonlinear partial differential equations (PDEs) governing fluid flows and related physical phenomena. The core research focuses on four primary problem...
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 detection, cybersecurity, and other fields. Key technical thrusts include establishing the theoretical performance of score-based change detection methods, developing robust algorithms under modeling uncertainty, and enabling distributed change detection across multiple data sources. The research outputs will provide the wider scientific and engineering community with new tools for solving complex change and anomaly detection problems. The project period runs from May 2024 to April 2027, and no subawards are planned under this award.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 4/16/24 |