Dr. LAU Yiu Keung Raymond
RAYMOND Y.K. LAU is an Associate Professor in the Department of Information Systems at City University of Hong Kong. He has worked at the academia and the ICT industry for over 35 years. He is the author of more than 200 refereed international journals and conference papers. His research work has been published in renowned journals such as MIS Quarterly, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Intelligent Systems, ACM Transactions on Information Systems, INFORMS Journal on Computing, Production and Operations Management, Journal of MIS, etc. His research interests include Financial Technology (FinTech), Social Media Analytics, Big Data Analytics, and Artificial Intelligence (AI) for Business. He served at the editorial board of the special issue "Fintech – Innovating the Financial Industry Through Emerging Information Technologies" for Information Systems Research, and the editorial board of the special issue "Big Data and Analytics in Networked Business" for MISQ. He is a senior member of the IEEE and the ACM, respectively.
Dr. Lau’s research philosophy lies on the notions of vision, persistence, flexibility, and collaboration. Research with great impact comes from: 1) the vision to establish long-term meaningful research goals which are achieved via inter-related short-term studies; 2) the persistence to continuously conduct indepth inquiries without immediate rewards; 3) the flexibility to adapt to changes in a research environment; 4) the collaboration with researchers across disciplines by using a variety of research methods and research perspectives. Dr. Lau’s research interests encompass artificial intelligence (e.g., deep learning, machine learning, topic modeling, ontology learning, image analysis, etc.), and its applications to business, social media analytics, big data analytics, and financial technology (fintech). Specifically, he often applies the design science research methodology to design novel IT artifacts, and evaluate the utilities of these IT artifacts through econometric-driven empirical analyses under a variety of real-world business contexts.
|Member||Association of Information Systems|
|Professional Member||Australian Computer Society|
|LIFE Full Member||Hong Kong Computer Society|
|Top 2% most highly cited scientist||Stanford University (2021)|
|Top 2% most highly cited scientist||Stanford University (2020)|
|Best Paper Award, PACIS 2014||AIS|
|University Teaching Excellence Award 2009/10||City University of Hong Kong|