Research

AREA 1: AI/DEEP LEARNING FOR BIG DATA

Our lab researches effective deep learning for uncovering dynamics hidden in the big data. Cutting-edge deep learning technologies, such as Convolutional Neural Network, Long Short-term Memory, Deep Reinforcement Learning, Deep Autoencoder, Generative Adversarial Network, and more have been thoroughly researched and implemented to deal with diverse kinds of challenges in big data, like multimodal, noisy, highly nonlinear, abnormal, and long-term dynamics.

AREA 2: EFFICIENT COMPUTING FOR BIG DATA

With deep understanding and research of the learning principles of AI models, our lab advances efficient edge computing and cloud computing for big data learning. Innovative research topics include: 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 and advance real-world, real-time practices, deployment and applications of AI algorithms.

AREA 3: WEARABLE COMPUTER & IOT SENSOR FOR BIG DATA

Our lab also designs and builds wearable computers and IoT sensing devices, to capture real-time long-term big data from human, environment, and generally the physical objects and world. Novel devices and software/hardware systems are customized for multimodal, wireless, ultra-low-power and intelligent perception of different kinds of biomedical and physical dynamics, which establish the big data and are then learned by our AI algorithms and processed by our computing platforms.

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.