This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $300,000 in funding to Trustees of Dartmouth College from July 2025 to June 2027. The project aims to design "resilient data stream algorithms" that are less dependent on assumptions and more reliable in practical applications. Key focus areas include developing adversarially robust algorithms, parameter-free and non-adaptive...
This $299,886 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new big data algorithms that are robust to adversarial input. The award supports research to address emerging vulnerabilities in areas such as black-box streaming algorithms, white-box streaming algorithms, and adaptive data analysis with bounded space. This work will focus on improving the reliability, security, and trustworthiness of...
The National Science Foundation (NSF) awarded a $174,671 Computer and Information Science and Engineering (CFDA 47.070) Program grant to Trustees of Clark University to develop a family of flexible, future-proof streaming algorithms for data stream monitoring applications. The project aims to establish common design frameworks for fundamental streaming algorithms like Bloom filters to enable a one-size-fits-all approach that can be reconfigured for diverse measurement tasks. This includes...
This $376,103 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research at Northeastern University to develop high-performance, space-efficient, and practical adaptive data filtering algorithms. The research aims to create filters that can adaptively change their representation to improve performance for streams of queries, especially for skewed or adversarial data workloads. The project will...
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...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $224,999 in funding to the Board of Regents of the University of Nebraska to support research on new directions in data streaming models and algorithms. The key research themes include: Initiating a study of a new "right to forget" data stream model to address privacy concerns and the explosive growth of data generation. Exploring the...
This National Science Foundation project grant of $186,123 awarded on July 1, 2022 will support the development of new streaming and sketching algorithms applicable to networking, machine learning, astronomy, and statistical inference. Funded under the Computer and Information Science and Engineering program, the award to Rice University will advance core objectives of the program by supporting investigator-initiated research in computing and information science. Specifically, the principal...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $600,000 in funding to the University of California, Berkeley (UC Berkeley) to conduct research on streaming algorithms and data sketching. The project aims to make progress on fundamental problems in streaming, such as heavy hitters, quantiles, moment estimation, and sampling from data streams. The work will involve developing...
This $255,353 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant awarded to Northeastern University will fund research on sublinear-time graph algorithms. The project aims to develop more efficient and optimal sublinear-time algorithms for foundational graph problems, understand the limitations of these algorithms through query lower bound analysis, and explore connections to other computation models like dynamic, parallel, and...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $299,996, supports a collaborative research project titled "CIF: SMALL: New Frontiers of Approximate Message Passing: Distributed Processing, Stochastic Updates, and Non-Asymptotic Bounds." The project, awarded to Trustees of Boston University, aims to develop novel iterative algorithms and a theoretical framework for efficiently processing massive...