AREA 1: AI/DEEP LEARNING FOR BIG DATA
Efforts are made on effective deep learning theories and architectures for uncovering subtle a dynamics hidden in the big data. Cutting-edge deep learning technologies, such as Graph Neural Network, Deep Reinforcement Learning, Deep Autoencoder, Generative Adversarial Network, Convolutional Neural Network, Long Short-term Memory, and more have been thoroughly researched and implemented to deal with diverse kinds of challenges in big medical data, among others.
AREA 2: EFFICIENT COMPUTING FOR BIG DATA
With deep understanding and research of the learning principles of AI models, efforts are made to advance efficient edge computing and cloud computing for big data learning. Innovative research topics include: efficient computing, critical pattern learning, pertinence learning of big data, efficient computing, ultra-low-power computing, and more, which target the pressing challenges of big data mining on medical, among others.
AREA 3: WEARABLE COMPUTER & IOT SENSOR FOR BIG DATA
Efforts are also made on wearable computers and IoT sensing devices, to capture human big data. The multimodal, wireless, ultra-low-power and intelligent perception of different kinds of biomedical and physical dynamics, establishes the big data which is then learned by the designed AI algorithms.
RESEARCH MULTIDISCIPLINARY TECHNOLOGIES FOR SYSTEMATIC INNOVATIONS
Pervasive Embedded Intelligence; Artificial Intelligence; Deep Learning; Machine Learning; Data Mining; Edge Computing; Efficient Computing; Wearable Computer; Wearable Intelligence; IoT Sensing; Connectivity; Biomedical Sensing Devices; Signal Processing; Image Processing; Smart Health/Home/World Big Data.
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