Recent advances in computing technologies, such as smartphones, Web 2.0 services, Internet of Things (IoT), and wearable devices, have substantially facilitated networked collaboration among people (e.g., social networks, Wikipedia, social Q&A) and also enabled various ubiquitous computing services (such as location-based services and smart home services).
KAIST Interactive Computing Lab’s major research area has been designing, evaluating, and understanding ubiquitous computing and social computing systems that are often situated in real social contexts and are made to appear anytime and everywhere. In particular, our research focus has been on the design of socially empowered, intelligent knowledge service systems that promote knowledge sharing, decision making, and wellbeing. Furthermore, we have been building enabling technologies that are essential for providing intelligent knowledge services (e.g., vibration sensing, location sensing, activity recognition, wireless networking).
Our research methods include analyzing multiple data sources ranging from sensor data to interaction data to understand user needs and find systems design implications, and building/deploying novel ubiquitous and social computing systems. The data collection step involves sensor data acquisition from wireless sensors and wearable devices; the data analysis step requires statistical machine learning and data mining techniques (to uncover generic patterns from data; and to automatically detect events and objects of interests); and the artifact building step aims to iteratively implement a working prototype and to evaluate its performance and user experiences in the wild.