Cooperative Agreement DESC0024442

This $246,516 project grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance the security of machine learning (ML) systems deployed in multi-tenant cloud FPGA (field programmable gate array) computing environments. The key research objectives are: (1) to systematically study and model adversarial hardware fault injection methods that could manipulate ML model parameters in a multi-tenant cloud FPGA...

This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...

This $738,226 federal Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program aims to establish an open-source ecosystem (OSE) that will revolutionize machine learning (ML) for scientific discovery. The project, led by the President and Fellows of Harvard College, will leverage the existing MLCommons community to develop and apply foundation models for science. Key objectives include creating benchmarks, datasets,...

This three-year, $300,000 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), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....

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 $300,000 to Arizona State University to develop a machine learning framework for training models across hospitals on electronic health records without sharing patient data. The framework aims to address fairness and mitigate biases by training representation learning algorithms jointly across multiple...

The National Science Foundation awarded $235,438 under the Computer and Information Science and Engineering federal grant program to develop interpretable and fair machine learning frameworks, algorithms, and methodologies. The two-year project grant aims to address concerns that machine learning algorithms can inadvertently amplify human biases by funding research to design scalable, data-driven methods with provable fairness guarantees. Specifically, the grant will support the development of...

This $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Northeastern University to improve the security of machine learning models in multi-tenant cloud field programmable gate array (FPGA) environments. The three-year award aims to advance understanding of vulnerabilities in cloud-FPGAs shared by multiple tenants, where a malicious actor could potentially manipulate another tenant's machine learning...

This three-year, $597,194 National Science Foundation project grant supports research at the Toyota Technological Institute at Chicago to advance the foundations of societal machine learning. The goals are to provide guarantees of fairness, accuracy, and positive societal impacts for machine learning systems used in applications impacting people. Key research areas include understanding fairness in machine learning contexts, especially regarding biased training data and multi-stage decisions;...

This $299,918 Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund the development of portable machine learning models to support nuclear physics experiments at the Facility for Rare Isotope Beams (FRIB) in Michigan. The project will create pre-trained machine learning models using self-supervised techniques that can be quickly adapted by FRIB users for...

This $625,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into end-to-end fairness for algorithm-in-the-loop decision making in the public sector. New York University will lead the development of novel methods for identifying and correcting biases in massive, multivariate data used for algorithmic decision making. This will include building models to represent human decision making processes, auditing tools to...