This $575,000 project grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant will support collaborative research at Columbia University focused on developing algorithmic and machine learning tools to improve the quality and representativeness of data used in high-stakes decision-making domains like healthcare, finance, and digital services. The key objectives are to: (1) analyze how individual data...
This $425,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research project titled "Incentives and Interventions for Robust Networked Data Exchange". The project aims to develop new theoretical foundations and algorithmic methods that improve data quality through incentives rather than constraints. The research will analyze how individual data providers, data platforms, and...
This $198,000 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Chicago. The grant supports the development of methodologies and programming frameworks for designing and implementing data-sharing market systems. The technical activities include designing and implementing a data-sharing programming framework, analyzing constraints and invariants of data flow control to aid...
The National Science Foundation (NSF) awarded a $299,993 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Chicago. The grant supports a collaborative research project on the "Foundations of Few-Round Active Learning" in supervised machine learning. The key objectives are to advance active learning algorithms and improve understanding of their capabilities in scenarios with limited interaction rounds. The research aims...
This National Science Foundation (NSF) Project Grant award, funded under the Engineering program (CFDA 47.041), supports research to develop a novel framework for privacy-aware and fair data acquisition in multi-agent distributed systems. The $439,961 award to the University of California, Santa Barbara aims to create fair incentives for strategic agents to contribute an appropriate share of private data, enabling efficient and safe operation of critical distributed systems like autonomous...
This $346,500 three-year Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund research at The Ohio State University examining the long-term impacts of fair machine learning under strategic individual behavior. The researchers will establish an analytical framework to characterize complex sequential interactions between individuals and machine learning systems over repeated interactions. This framework aims to enable...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering (CFDA 47.070) program, provides $274,648 to Clemson University to develop heterogeneous architectures for collaborative machine learning. The research aims to address key challenges in efficiency, adaptivity, and privacy preservation when deploying collaborative machine learning models across diverse hardware platforms. The project will design specialized neural network...
The National Science Foundation (NSF) awarded a $174,200 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of North Carolina at Chapel Hill (UNC-CH) to develop new resource allocation policies for optimizing the scheduling of parallelizable machine learning (ML) training workloads on shared hardware clusters. The goal is to enable the rapid and efficient training of highly accurate ML models using limited computing resources. This...
This National Science Foundation (NSF) Project Grant award to Purdue University, under the Computer and Information Science and Engineering program (CFDA 47.070), focuses on developing novel technologies to enable robust, fair, and explainable data-driven decision-making systems. The $466,411 award, effective July 1, 2023 through June 30, 2028, will fund research to: 1) detect and mitigate biases in machine learning model outcomes, 2) assess the validity of data for learning fair and trustworthy...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $470,670 to the University of Wisconsin-Madison to investigate the incentives and mechanisms that influence participation in data sharing and collaborative machine learning. The project aims to create protocols that can balance responsibilities and benefits among organizations, while preventing strategic behavior that could undermine data sharing and collaboration. The research will explore multi-round data exchange scenarios where participants adapt their strategies over time under uncertainty. The project's findings are expected to broadly contribute to the fields of machine learning and game theory. Additionally, the award will support the development of new educational offerings, such as courses, workshops, and research opportunities for undergraduate and high school students.