Our mission is to pioneer system-centric AI research that spans a broad spectrum of mobile edge research from energy-efficient hardware to the human-centered applications. Our core philosophy centers on bridging the gap between theoretical AI models and highly efficient, deployable systems on real-world mobile devices.
We are the Mobile Embedded AI Systems Lab at Ewha Womans University. Our research bridges the gap between theoretical AI models and real-world deployment, tackling the full stack of mobile and embedded systems — from energy-efficient hardware to intelligent, human-centered applications.
Our work spans three core areas. In on-device AI, we develop resource-efficient inference techniques and adaptive DNN architectures that run directly on mobile devices, addressing privacy, latency, and connectivity constraints across the Android software stack. In intelligent energy storage management, we apply machine learning to battery capacity estimation, hybrid storage system design, and novel sensing techniques for devices ranging from smartphones to electric vehicles. In battery-less IoT systems, we pursue energy-autonomous sensor nodes powered by light harvesting, combining low-power hardware design with reinforcement learning-based adaptation to enable reliable operation in smart farm and other next-generation IoT environments.
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