User experience researcher. Interaction designer. Inventor.
I am a PhD candidate at the School of Information, University of Michigan, working with Dr. Mark W. Newman. I received my Master's degree in Human-Computer Interaction from the University of Michigan's School of Information. I worked as a search service and advertising specialist at Naver.com.
As a human-computer interaction researcher and interaction designer, I design, deploy, and evaluate novel technologies to explore new ways and opportunities to support people’s everyday tasks and needs. Using user-centered, field-based research methods, I investigate user interaction and experience with emerging technology, develop prototypes, and design strategies, and evaluate them in real-world contexts. My research has been focused on the following areas:
Intelligent Systems for Sustainability
Designing features and human-system interactions with the goal of saving energy
Designing eco-interaction technology:
* BEST PAPER AWARD * CHI 2014
One area where smart home devices promise to deliver great benefits is in the control of home heating and cooling systems. In this research project, we sought to inform the design of future eco-interaction systems by investigating users’ experiences with home heating and cooling systems. We conducted a qualitative study with both manual and programmable thermostat users and another with Nest thermostat users between 2011 and 2013. Both studies consisted of interviews augmented by a diary study, for a total of 90 interviews and 508 diary entries. A key finding was that the Nest impacted users’ pattern of home heating and cooling control, but only for a while, and caused new problems in unrealized energy savings. In leveraging these findings, we created a set of design implications for the design of features and human-system interactions with the goal of saving energy.
ThermoCoach: Personalized thermostat recommendations:
In this project, I collaborated with the research team at University of Virginia. We designed and developed a new system, called ThermoCoach, which provides personalized and actionable recommendations for thermostat use. The system senses human occupancy patterns in a home and emails the household suggested set-point schedules that can be modified or activated with the click of a button. We conducted a 12-week field deployment in 40 homes. Results indicated that ThermoCoach saved 4.7% more energy than a manually programmable thermostat and 12.4% more energy than a fully automated thermostat. Currently, we are analyzing data to investigate how users responded and interacted with the system and how recommendations for thermostat scheduling impacted users' daily thermostat control practices.
Inferring thermal comfort at home using commodity sensors:
Emerging wearable and smart home sensing devices offer the opportunity to develop new models of thermal comfort based on data collected in-situ. To explore this opportunity, we deployed a sensing system in seven homes and collected self-report data from 11 participants for four weeks. Our system captures many factors employed in previous thermal comfort research, as well as new factors (e.g., activity level, sweat level). Machine learning-based models derived from the collected data show improvement over previous techniques, however significant prediction errors remain. In analyzing these errors we identify six problems that pose challenges for inferring comfort in the wild. Based on our findings, we suggest techniques to improve future in-situ thermal comfort modeling efforts.
Smart home and devices
Improving end-user understanding and control of everyday smart devices
Learning from a learning thermostat:
Everyday systems and devices in the home are becoming smarter. In order to better understand the challenges of deploying an intelligent system in the home, we studied the experience of living with an advanced thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. We conducted interviews with 23 participants, ten of whom also participated in a three-week diary study. Our findings showed that while the Nest was well-received overall, the intelligent features of the Nest were not perceived to be as useful or intuitive as expected, in particular due to the system’s inability to understand the intent behind sensed behavior and users’ difficulty in understanding how the Nest works. A number of participants developed workarounds for the shortcomings they encountered. Based on our observations, we proposed three avenues for future development of interactive intelligent technologies for the home: exception flagging, incidental intelligibility, and constrained engagement.
End-user difficulties in the assessment of personal tracking device accuracy:
Personal tracking technologies allow users to monitor and reflect on their physical activities and fitness. However, users are uncertain about how accurately their devices track their data. In order to better understand this challenge, we analyzed 600 product reviews and conducted 24 interviews with tracking device users. We documented the methods users used to assess accuracy of their tracking devices and identified seven problems they encountered. We found that differences in users’ expectations, physical characteristics, types of activities and lifestyle led them to have different perceptions of the accuracy of their devices. With the absence of sound mental models and unclear understanding of the concepts of accuracy and experimental controls, users designed faulty tests and came to incorrect conclusions.
Developing a context-aware mobile application for wayfinding
Talking Points is collaborative student project at the University of Michigan whose aim is develop a prototype urban orientation and contextual information system. By using a mobile application to read Bluetooth tags or GPS coordinates positioned around a city, user generated location information is presented to the user via either an audio or visual modular interface.
Supporting independent wayfinding for people with visual impairments:
In this project, we designed Talking Points 3, a mobile location-aware system for people with visual impairments that seeks to increase the legibility of the environment for its users in order to facilitate navigating to desired locations, exploration, serendipitous discovery, and improvisation. We conducted user testing with eight legally blind participants in three campus buildings in order to explore how and to what extent Talking Points 3 helped promote spatial awareness for its users. The results shed light on how TP3 helped users find destinations in unfamiliar environments, but also allowed them to discover new points of interest, improvise solutions to problems encountered, develop personalized strategies for navigating, and, in general, enjoy a greater sense of independence.
Working at Naver.com, I co-invented 17 patents (13 granted, 3 abandoned, 1 rejected)
I was one of the three inventors who created the Naver.com's cost-per-click based advertising business model, Click Choice, which has been the main advertising system of Naver.com since 2005. It was essentially the first Korean “pay-per-click” advertising system. My role was to create a new business model and design an effective user interface for the web-based advertising system.
Naver Lab is a place to introduce Naver's latest innovative services. Users can play with experimental search services of Naver.com. As an assistant manager, I worked with over 50 developers and designers to launch Naver Lab and its first six beta search services, such as News Clustering Search, Face Detection Search, Sentimental Search, Language Convertor from Korean to English, Chinese and Japanese, Inflow Keyword Analysis for Blog and Forum.
Eye Tracking & Bucket Test
In designing and managing various advertising products, I had the valuable opportunity to study in depth a group of users’ behavior. Our team conducted eye-tracking research to see how users interact with the search results page. We executed a bucket test to experiment several kinds of UI for the ad product. Through those 2 researches, we found that the click rate is very dependent on the types of keyword. Finally, we changed the UI of the keyword ad based on the findings from the research and the click through rate was raised by 10%.
School of Information
105 S. State, Ann Arbor