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  • We're looking for MS or PhD Students for Spring 2023 (Deadline: July 8, 2022) - If you're interested in pursuing MS or PhD please send an email to Prof. Lee (uclee@kaist.ac.kr) - please attach a short description about your interests, transcript, and resume. 2023년도 석사 또는 박사과정 학생 선발합니다. (KAIST 장학생 및 국비로 선발 가능). 실험실에 관심이 있는 학생은 관심사, 성적표, 레주메를 이메일로 보내주세요. (2022년도 가을 학기는 학생선발을 하지 않습니다).
  • We're looking for Summer interns (2022) (Deadline: May 9, 2022) (Internship Period: 2022.7.2-2022.8.26) Call for Interns Application Form 2022년도 여름방학 인턴 모집합니다. 관심있는 학생은 슬라이드에 있는 주제를 참고하셔서 온라인 신청해 주시기 바랍니다.
Interactive Computing Lab
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 is positive computing, which aims to use computing technologies to support wellbeing and human potential. In particular, we focused on leveraging ubiquitous computing technologies that have sensing, networking, and computing capability (e.g., smartphones, wearables, and Internet of Things) to better understand and deal with threats to the well-being of users, ranging from technology dependence (e.g., addiction, productivity loss, technostress) to mental/physical/social problems (e.g., lack of physical activity, social isolation, and depression). We as researchers in the HCI field, are well aware of such threats to users’ well-being and have always wanted to make not only scholarly contributions but also societal impact by making our research outcomes accessible to the people who are in need. For this reason, the major research efforts undertaken by us have been on designing, building, and evaluating novel positive computing systems that help improve productivity and physical activity using interactive technologies.

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.
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