The National Science Foundation Division of Computing and Communication Foundations awarded Oberlin College $239,830 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research from October 1, 2022 to September 30, 2025. The award will fund the development of a new theory of computational learning adapted to economic environments where participants may strategically manipulate data. Specifically, the research seeks to design algorithms...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program totaling $599,996 will fund research into the development of learning-augmented mechanisms from October 1, 2022 to September 30, 2025. The goal of the research is to extend the framework of algorithms with machine-learned predictions to the design of mechanisms in the presence of strategic agents. Specifically, the award recipient Drexel University will consider the design and...
This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $287,594 to support collaborative research addressing challenges in learning and inference from large-dimensional data. The awardee, The Trustees of the University of Pennsylvania doing business as the Clinical Practices of the University of Pennsylvania, will conduct the research from January 2022...
This two-year, $299,998 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research quantifying the fair value of data and privacy in distributed learning environments. The grantee, the Regents of the University of California at Berkeley, will develop a framework for systematically quantifying the value of data at various privacy levels using techniques from economics, game theory, optimization, machine learning and statistics....
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...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $599,986 to Stanford University for research titled "Machine Learning with Behavioral and Social Data." The five-year award beginning in August 2022 will support the development of new machine learning algorithms that model human decision-making descriptively based on behavioral data. The researcher aims to build on recent advances in modeling choices as driven by...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $279,959 to Carnegie Mellon University to advance the frontiers of differential privacy algorithms for private learning and synthetic data generation. The 5-year research project aims to develop a theoretical framework to better capture practical privacy scenarios, design practical privacy-preserving algorithms, and create auditing...
This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
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 $598,448 Project Grant, awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports the development of theoretical and algorithmic foundations for online learning and decision-making involving sequential data under unknown stochastic models. The project aims to advance representation learning, statistical inference, and learning methodologies for real-time monitoring and control of critical infrastructure networks,...