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Michael Chau is currently an Associate Professor in the HKU Business School and the Warden of Lee Chi Hung Hall at the University of Hong Kong. He received a Ph.D. degree in Management Information Systems from the University of Arizona and a B.Sc. degree in Computer Science (Information Systems) from the University of Hong Kong. His research interests include business analytics, artificial intelligence, web mining, social media, electronic commerce, healthcare informatics, and security informatics.
He is the author of more than 140 articles and has been ranked in a research productivity study as the #14 most productive researcher in the field of information science in the period 1998-2007.
Michael has taught a wide range of courses at HKU, including database management, computer networking, business analytics and big data, artificial intelligence, project management, spreadsheet modelling, and computer programming.
Dr. Chau’s research has appeared in more than 150 publications. Please refer to https://pweb.fbe.hku.hk/~mchau/publications.html for a complete list.
Dr Michael Chau’s study uses the machine learning and rule-based classification components of artificial intelligence to find people online who are in emotional distress. Using these techniques may help health professionals locate struggling individuals faster, getting help to them before it’s too late.
Many people face problems of emotional distress. Early detection of high-risk individuals is the key to prevent suicidal behavior. There is increasing evidence that the Internet and social media provide clues of people’s emotional distress. In particular, some people leave messages showing emotional distress or even suicide notes on the Internet. Identifying emotionally distressed people and examining their posts on the Internet are important steps for health and social work professionals to provide assistance, but the process is very time-consuming and ineffective if conducted manually using standard search engines. Following the design science approach, we present the design of a system called KAREN, which identifies individuals who blog about their emotional distress in the Chinese language, using a combination of machine learning classification and rule-based classification with rules obtained from experts. A controlled experiment and a user study were conducted to evaluate system performance in searching and analyzing blogs written by people who might be emotionally distressed. The results show that the proposed system achieved better classification performance than the benchmark methods and that professionals perceived the system to be more useful and effective for identifying bloggers with emotional distress than benchmark approaches.
The health and medical needs of an ageing population mean Hong Kong has to store up increasing amounts of fresh blood products. This year alone, the Hong Kong Red Cross Blood Transfusion Service (BTS) needs 3.4 per cent more units of whole blood, plasma and platelets than it collected last year, when demand increased by 4.4 per cent. Added to that growing demand is the fact that donations from first-time donors have fallen, in part because the revised school curriculum means Form 7 students are now spread out in universities and the workplace, rather than easily targeted in a school. So where can additional donors be found? To help find an answer, the BTS has been turning to Dr Michael Chau, Associate Professor in the Faculty of Business and Economics, whose research focuses on data mining and analysis.
It’s no secret that private bloggers tend to speak freely in their conversations about firms and their products. But how can firms tap into blogs and build their Business Intelligence?
