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. Specifically, the project will create a bi-level optimization framework for robust machine learning with defenses against adversarial examples and distribution shifts. It will also build an end-to-end pipeline for evaluating model robustness over the full life cycle. Additional algorithms will be developed to jointly improve robustness and fairness under practical constraints such as limited sensitive attribute data or scarce training resources. Finally, scalable computational methods grounded in optimization theory will be designed to achieve accurate, resilient, and high-throughput trustworthy learning. The university will disseminate shared toolboxes and benchmarks to broader machine learning communities upon completion in September 2025.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $300.0k | 8/31/22 |