This $240,000 Project Grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of computationally efficient algorithms to approximate the impact of removing data subsets from high-dimensional machine learning models. The research aims to advance scientific understanding of artificial intelligence, improve the robustness of decision-making systems, and contribute to the development of privacy-preserving...
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 $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 Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
This three-year, $292,495 project grant from the National Science Foundation's Division of Computing and Communication Foundations aims to develop new data science techniques for economic environments under the Computer and Information Science and Engineering program (CFDA 47.070). The recipient, Drexel University, will conduct research to design learning algorithms and systems that can effectively interpret data in strategic settings where participants may manipulate information in anticipation...
This National Science Foundation Project Grant of $123,115 awarded on March 15, 2022 will fund research into the value of consumer data for digital platforms. The research will be conducted under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) through February 2029 at Columbia University. Specifically, the research will investigate how the value of an individual consumer's data record is determined for an intermediary that uses it, such as an e-commerce platform's transaction...
This three-year, $260,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will fund research towards designing optimal statistical learning procedures through precise medium-dimensional asymptotic analysis. The grantee, Columbia University, will develop a novel analytical framework to quantitatively characterize the performance of diverse learning algorithms and provide guidance on...
This Project Grant award for $117,910 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop new statistical estimation methods and algorithms that can efficiently process complex, high-dimensional datasets. The research will focus on three key areas: (1) providing rigorous theoretical guarantees for the performance of high-dimensional statistical estimation techniques, (2) establishing computational limits and efficiencies for modern...
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 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...