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演講簡介：Anchored in electrical physiology (EP) and biomedical engineering (IBME), my research investigates neural communication and physiological integration to advance clinical technologies. This foundation drives our development of restorative medical prostheses (MP) and visual prostheses (VP) designed to address retinal degeneration by emulating human visual processing. Building upon these interdisciplinary pillars, my recent work bridges artificial intelligence (AI) with ultrasonic architectures to pioneer advanced sensing and actuation platforms. We developed a Physics-Informed Neural Operator (PINO) digital twin framework that significantly accelerates air-coupled ultrasound simulations, resolving data scarcity bottlenecks for AI training while preserving physical fidelity. For intelligent transportation, we introduced a generative adversarial network (GAN) augmentation strategy that enhances acoustic environment recognition in electric vehicles under imbalanced data conditions. Furthermore, we engineered a closed-loop airborne ultrasound system that utilizes acoustic echoes and machine learning for the real-time tracking and continuous focused sonication of freely moving targets without camera assistance. This capability is supported by our scalable, reconfigurable dual-mode ultrasonic phased array, which enables concurrent transmission and echo reception. Ultimately, integrating AI algorithms with reconfigurable ultrasonic hardware empowers us to translate theoretical biomedical concepts into deployable digital twins, resilient acoustic sensors, and non-contact neuromodulators.