This $123,859 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will enable the University of California, Santa Barbara (UCSB) to develop a closed-loop machine learning (ML) pipeline that iteratively refines training data collection to improve the generalizability of ML models for network operations. The key objectives are to: (1) design a programmable data-collection platform to acquire flexible and scalable training data across diverse network environments, and (2) develop methodologies using explainable ML techniques to detect and address underspecification issues in network models. This closed-loop approach aims to ensure that ML models adapt over multiple iterations, mitigating learning shortcuts and spurious correlations that can degrade performance in real-world settings. The project outcomes, including datasets, software, and model implementations, will be made publicly available to benefit researchers, network operators, and educators.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $123.9k | 4/1/25 |