Welcome to the IIIS Lab!

The lab’s research activities focus on web intelligence, online content search, understanding, mining, and recommendation, with particular emphasis on information retrieval and knowledge discovery regarding biomedical contents. The lab has conducted extensive studies on automatic document content understanding, text mining, and text information fusion from multiple sources. The lab’s research portfolio further includes projects on artificial intelligence, computer graphics and visualization techniques, human computer interaction, digital art and design, and calligraphy. The lab holds 25 approved invention patents and 55 registered software copyright licenses.

Recent externally funded projects of the lab pursue big data computing for cancer informatics. The World-Wide Web (Web 1.0) and online social media (Web 2.0) have revolutionized the ways medical knowledge is disseminated and health information is exchanged and shared among patients, supporters, and health care providers. Online patient communities have been expanding at an impressive rate with millions of active participants from all age groups. Recent studies on researching and analyzing social media contents for health-related applications show that this uprising cyber-trend leads to valuable knowledge, traditionally acquired with scientific methods such as observational epidemiological studies. This new mode for information acquisition is particularly advantageous for studies requiring long period of data curation.

Our research leverages the power of online contents, including user-generated contents on social network sites, to tackle complex migration patterns and their effect on environmental cancer risk. The rich amount of personal information shared openly among cancer patients and cancer-free people online is effectively mined to generate new knowledge on the topic, which cannot be easily uncovered with conventional migrant studies in our modern economy with population mobility patterns far more complex and dynamic than those observed in the past.