Ph.D. in Computer Science,
- 2001 Chonbuk National University, Computer Engineering B.E.
- 2003 KAIST, Computer Science M.S.
- 2008 UCLA, Computer Science Ph.D.
- 2016.3-Current, Associate Professor, KAIST Industrial & Systems Engineering Dept.
- 2013.9-2016.2, Associate Professor, KAIST Knowledge Service Engineering Dept.
- 2010.8-2013.8, Assistant Professor, KAIST Knowledge Service Engineering Dept.
- 2009.8-2010.8, Member of Technical Staff, Bell Labs, Alcatel-Lucent
- 2008.9-2009.7, Postdoc Researcher, UCLA
- 2007.6-2007.9, Research Intern, IBM T.J. Watson Research Center
- Our group’s research fields include (1) Human-Computer Interaction, (2) Ubiquitous Computing, (3) Social Computing, and (4) Data Science and Internet of Things (IoT). Our research goal 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).
Mobile Health with Data Science and Wearables
Our lab maintains the KAIST DrM health platform for collecting and visualizing mobile health data. We distributed various wearable devices such as Apple Watch, Samsung Gear S2, and Fitbit Charge and have been collecting activity tracker data (e.g., step counts, heart rates, and GPS traces) from a large number of people in campus. We have been analyzing this longitudinal activity tracking dataset to understand wearing behaviors and user experiences of wearable devices. We recently extended our platform to collect detailed user interaction data from smart devices. This will help us to uncover behavioral markers of mental and physical well-being metrics (e.g., depression, stress) and to design novel mobile intervention software. Furthermore, we have been exploring how novel wearable technologies (e.g., smartphone apps and VR headsets) can be used to innovate diagnosis and treatment of various health problems by supporting in-situ measurement and data processing.
Problematic Smartphone Use: Data Analytics and SW-based Intervention Services
Concomitant with the explosive increase in the popularity of smartphones in recent years, negative aspects of their usage, such as social conflicts, sleep deprivation, and attention deficit, have emerged. Our research has been focused on dealing with problematic usage behaviors by applying computational techniques and developing computer-assisted intervention methods. Our major research contributions include 1) identification of usage patterns related to problematic smartphone usage, and development of automatic problematic usage classification systems (CHI 2014); 2) development and validation of several computer-assisted smartphone overuse intervention services that leverage social support. For example, NUGU (CSCW 2015) and FamiLync (Ubicomp 2015) were designed to foster social awareness and improve self-regulation of smartphone use by considering key social contexts (e.g., among friends or family members). Lock n’ LoL (CHI 2016, Best Paper Award) was designed to mitigate social exclusion due to smartphone distraction during group activities by supporting synchronous awareness to limit usage.
Designing Serious Games for Well-being and Education
Our lab has been working on designing and evaluating various serious games whose primary goals are to promote physical well-being using computer games and gamification techniques. Designing interactive technologies that can improve physical wellbeing has been an active area of research in the field of HCI and health sciences. Our lab has investigated how to gamify solitary exercises such as stationary cycling and running on treadmills (Mobisys 2012; CSCW 2013; CHI 2014). We have also examined how interactive technologies can enrich group fitness exercises such as group swimming (Sensys 2014; CHI 2016). In addition, we explore the usability and user experiences of integrating fitness equipment into a workstation environment (Ubicomp 2016).
Smart Factory: Machine Condition Monitoring
Monitoring machine condition is critical for maintaining the assets in any manufacturing process. Breakdown can cause serious consequences such as production loss or costly repairs. The goal of this project is to diagnose the health of manufacturing equipment and to predict the remaining useful life (RUL) of machine parts by analyzing vibration sensor data.
Novel Mobile Service Design and Evaluation
Our lab has been exploring how novel sensing and collaboration technologies can innovate conventional services, ranging from sharing color reviews of products purchased online (MobileHCI 2015) to collaborative photographing (ACM CHI 2017) and community policing (ACM CHI 2016, 2017). Here, we explain our recent studies on community policing which is defined as the police's efforts to partner with community members and civic organizations to enhance a wide range of neighborhood safety issues (e.g., crimes, norms), by letting them to participate in various prevention, problem solving, and law enforcement activities. In this project, we perform an exploratory study of designing and evaluating new forms of community policing by leveraging pervasive recording technologies. In particular, we study privacy concerns and motives behind video sharing (ACM CHI 2016). Furthermore, we designed and evaluated a mobile app that helps citizen record traffic violation with their smartphones and report the recorded videos to the police (ACM CHI 2017). We have been incorporating advanced computer vision techniques into Mobile Roadwatch for automatically capturing traffic violations and safety risks, including potholes and obstacles.
- Lee et al., "Hooked on Smartphones: An Exploratory Study on Smartphone Overuse among College Students," ACM SIGCHI Conference on Human Factors in Computing Systems (CHI'14), Toronto, Canada, April 26-May 1, 2014
- Choi et al., "MobyDick: An Interactive Multi-swimmer Exergame," In Proc. of the 12th ACM Conference on Embedded Networked Sensor Systems (SenSys'14), Memphis, TN, November 3-6, 2014
- Ko et al., FamiLync: Facilitating Participatory Parental Mediation of Adolescents' Smartphone Use," In Proc. of The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'15), Osaka, Japan, Sep. 7-11, 2015
- Ko et al., "Lock n' LoL: Group-based Limiting Assistance App to Mitigate Smartphone Distractions in Group Activities," ACM CHI 2016, San Jose, CA, USA, May 7-12
- Park et al., "Motives and Concerns of Dashcam Video Sharing," ACM CHI 2016, San Jose, CA, USA, May 7-12
- Ko et al., Understanding Mass Interactions in Online Sports Viewing: Chatting Motives and Usage Patterns," ACM Transactions on Computer-Human Interaction (TOCHI), Volume 23 Issue 1, January 2016
- 2014-2015 AAAI ICWSM, Program Committee
- 2015 ACM Sensys’15, Finance Co-Chair
- 2015 IEEE ICMU’15, Program Committee Co-Chair
- 2015 ACM CHI’15, Program Committee
- 2015-2016 ACM Ubicomp’15, Program Committee
- 2016 AAAI’16, Program Committee
- 2016 AAAI ICWSM’16, Senior Program Committee
- 2016 MobilWare’16, Program Committee Co-Chair
- 2016 ACM CHI’17, Program Committee
- CoE203 IT Convergence Project
- KSE624 Mobile and Pervasive Computing for Knowledge Services
- KSE652 Social Computing Systems Design and Analysis
- KSE631 Content Networking
- Methods of locating data spots and networks and user equipment for using the same, US8914041 B2, 2014
- 다중 카메라 디바이스를 활용한 멀티미디어 촬영 방법 및 그 시스템, 10-1511868-0000, 2015
- 개인화 수영 영법 실시간 검출 방법 및 시스템, 10-1579380-0000, 2015
- 모바일 문서 캡쳐를 위한 카메라 탑다운 앵글 보정 방법 및 장치, 10-1588136-0000, 2016
- 근거리 그룹 사용자들의 사진촬영 상황정보 기반 실시간 유사사진 탐색 제공 방법 및 시스템, 10-1615475-0000, 2016