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 $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 $108,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support a collaborative research project to develop a unified framework for analyzing adaptive stochastic optimization methods for machine learning applications.
The research aims to create adaptive optimization algorithms with strong theoretical guarantees that can reduce the extensive computational overhead often required by...
This $200,208 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research by New York University (NYU) on history independence (HI) in algorithmic performance. The project aims to demonstrate how HI can be used as a powerful analytical tool for designing randomized data structures and algorithms, especially in the presence of oblivious adversaries. The research will investigate HI...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $255,353 to Northeastern University for research on sublinear-time graph algorithms. The project aims to develop more efficient sublinear-time algorithms for foundational graph problems, understand the limitations of such algorithms through lower bound analysis, and explore connections to other computational models. Key research objectives...
The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, Berkeley to conduct research on improving the fundamentals of data streaming algorithms. The project aims to make progress on core problems in streaming data analysis, such as heavy hitters, quantiles, moment estimation, and sampling from data streams. The research will explore new approaches to...
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...
This $175,000 federal Project Grant award under the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program, awarded on October 1, 2024, supports collaborative research on new directions in data stream computing models and algorithms. The key research themes include: (1) studying a "right to forget" data stream model to address data privacy concerns, (2) exploring pseudodeterministic computations in streaming algorithms, and (3)...
This $122,000 NSF CAREER Award under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop new mathematical tools for analyzing sampling algorithms used to manipulate and understand complex, high-dimensional probability distributions. The project aims to address several foundational open problems in theoretical computer science related to the design of efficient sampling algorithms with rigorous guarantees. The research will be conducted in...
This Project Grant award, provided by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support research on fairness-aware data structures for approximate query processing. The $499,999 award, effective August 15, 2024 through July 31, 2027, aims to design data structures with theoretical guarantees that achieve group fairness across domains such as hashing, membership estimation, aggregate query estimation, and approximate...