Prof. Yan's group is composed of 2 professors, 4 associate professors and 4 assistant professors. Research in the group revolves around the scientific and technological issues related to biomedical informatics, especially those involving computer aided interpretation of biomedical signals and images. It overlaps a number of areas in biomedical imaging and medical instrumentations. Most of Prof. Yan's graduate students and research staff collaborate with a number of universities and major IT companies.

Major directions include:

(1) Early diagnosis technologies: Advanced noninvasive medical imaging modalities and computer aided diagnosis for structural and functional images in the level of systems,organs, tissues, cells and molecules.

(2) Rehabilitation and assistive technologies: Hardware and software technologies on feedback type human-machine interfaces and games to improve patients' cognition and physical activities; long term wireless multi-physiological parameters monitoring technologies for aging populations.

Recent Works:

(1) Advanced digital medical imaging solutions. Researches in this topic include: Inventing novel technologies for early detection and diagnosis of Alzheimer's disease, breast cancer and atherosclerosis; developing more accurate, sensitive, and rapid high definition (HD) medical imaging solutions with own IP rights, e.g., the embedded video processing technology, the real-time image mosaic technology for multi-channels videos, and the parallel acceleration technology for full HD images.

Outcomes in this topic have already been implemented in: The full HD endoscope for otolaryngology-head and neck surgery, the super-high resolution laparoscopy, the novel ureterorenoscopy, and laser speckle imaging system.

(2) Multi-physiological parameter monitoring and rehabilitation technologies. Researches in this topic include: Inventing novel technologies for prevention and early detection of Alzheimer's disease, hypertension and diabetes; generating new technologies to prevent and diagnose cardiovascular and cerebrovascular diseases by combing multidisciplinary technologies (e.g., electrophysiological, medical clinical atherosclerosis and social emotional cognition technologies); developing long termw ireless multi-physiological parameters monitoring technologies, and neural information processing and functional rehabilitation technologies; exploiting portable,easy to use and efficient intelligent mobile medical monitoring terminals and the cognitive training system based on emotional perception.

Outcomes in this topic are expected to be implemented in: Sleep monitoring networks,regional integration rehabilitation training monitoring systems, home care and healthcare systems.