Project Grant 2442290
- This $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports research by the Regents of the University of California at Riverside to study security vulnerabilities in machine learning (ML) models. The project aims to understand how malicious actors could exploit unused parameters in trained ML models to install additional, potentially harmful functionality without detection. The research...
- 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...
- The National Science Foundation (NSF) awarded a $800,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Illinois Urbana-Champaign. The 3-year grant, effective September 1, 2024, focuses on enhancing the safety of large language models (LLMs) used in high-stakes applications. The project aims to develop quantifiable safety measures and algorithms to detect and mitigate unsafe behaviors in LLMs, such as providing false or...
- This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), aims to improve the security and resilience of machine learning (ML) software. The $262,828 award, issued on March 15, 2025, will support the development of methods for detecting and correcting non-functional vulnerabilities in ML libraries, such as denial-of-service attacks and side-channel attacks. This research...
- This $236,099 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is supporting research to develop robust optimization and machine learning algorithms capable of handling dynamic and uncertain data environments. The research aims to advance optimization techniques for fundamental supervised learning tasks, yielding computationally and data-efficient algorithms with provable error guarantees. This work will...
- The National Science Foundation (NSF) awarded a $1,182,881 Project Grant to the University of California, San Diego (UCSD) under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of new methods to ensure that machine learning models assigned to decisions such as lending and hiring can be changed through individual actions, protecting the right to access these services. The project will create techniques for (1) detecting...
- The National Science Foundation (NSF) awarded a $337,962 Computer and Information Science and Engineering (CFDA 47.070) Project Grant to the University of Illinois to expand its Breakthrough Tech AI program. The project, executed in collaboration with Cornell Tech and Hofstra University, aims to democratize access to high-quality AI education and significantly increase the number of underserved students, particularly women and other underrepresented groups, who receive training in artificial...
- 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 $471,529 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to expand the understanding of large language models (LLMs), a type of artificial intelligence (AI). The project at the Trustees of Boston University aims to move beyond identifying simple, binary concepts within LLMs and instead develop methods to discover and characterize more sophisticated, multi-dimensional...
- 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 $474,838 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The grant, awarded to the University of Illinois, supports research addressing the triple challenges of accuracy, robust generalization, and interpretability in machine learning (ML) models. The key technical aims of the project include: (1) developing a unified framework for analyzing and optimizing the trade-offs between statistical parity, equalized odds, and model accuracy in classification tasks; (2) creating theories and algorithms to ensure ML models can generalize well across diverse tasks and domains, particularly under distribution shifts; and (3) advancing data attribution techniques to enhance the interpretability of ML decision-making processes. The research outcomes will be integrated into undergraduate and graduate courses to bolster technical instruction on trustworthy ML. The project will be executed over a 5-year period from August 1, 2025 to July 31, 2030.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $474.8k | 7/26/25 |