5200 N Lake Rd, Merced, CA 95343

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The proliferation of the Internet of Things (IoT) and cloud services has given rise to the edge computing paradigm, where data is processed partly or entirely at the edge of the network, rather than solely in the cloud. Edge computing can address problems such as latency, limited battery life of mobile devices, bandwidth costs, security, and privacy. Typical applicable scenarios based on edge computing include video analytics, smart home, smart city, and collaborative edge. With advancements in deep learning techniques, research on using deep learning to develop intelligent edge systems is rapidly emerging. In this talk, I will discuss how deep learning can process data on individual edge devices with limited resources in real time. Additionally, I will explore how deep learning can leverage collaborative edge devices to provide improved services. I will use several critical IoT systems as use cases to illustrate our research concept, including video analytics, driving anomaly detection, arm posture tracking, and device orientation estimation.


Miaomiao Liu is a Ph.D. candidate in Electrical Engineering and Computer Science at the University of California, Merced, supervised by Professor Wan Du. Her research focuses on developing real-time, adaptive, and smart systems in the field of the Internet of Things.  Her research interests lie in the areas of wearable-based sensing, video analytics, mobile/edge computing, and on-device deep learning.

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