This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $200,000 to the University of Louisiana at Lafayette to develop novel techniques for analyzing spatial-temporal data in AI-enabled Internet-of-Things (AIoT) systems. The project aims to advance graph signal processing and graph learning methods to improve the interpretability and efficiency of AIoT data analysis. Specifically, the research will focus on: (i) topology sampling and pruning of single-layer graph models, (ii) multilayer graph models for spatial-temporal data processing, (iii) propagation behavior and dynamic graph evolution in AIoT, and (iv) parameter-efficient transfer learning for spatial-temporal signal processing. The project will also share research outcomes, algorithms, and interpretable AIoT solutions with the public, and offer educational opportunities for undergraduate and graduate students. This award supports the NSF's mission to advance scientific understanding and drive technological innovation with potential societal and economic impact.
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