I am an assistant professor at University of Maryland's College of Information Studies (a.k.a. "iSchool"). Before coming to Maryland, I was an assistant professor at Penn State's College of Information Sciences and Technology (2014-2017).

With a background in HCI, Health Informatics, and Industrial Design, I design, develop, and evaluate technology to help people become empowered individuals and make positive behavior changes through fully leveraging their personal data. I explore this topic in various contexts including the Quantified Self community, sleep and exercise, patient-clinician communication and data sharing, and personal data insights and visualization.

I received a PhD degree in Information Science from University of Washington, MS degree in Information Management and Systems from University of California, Berkeley and BS degree in Industrial Design from KAIST. You can see my CV.

I have openings for 2 graduate students and 2 undergraduate students for the 2017-2018 academic year.

I am seeking self-motivated, intellectually curious, and hard-working students focused in HCI research. The best way to contact me is by email. Ideal candidates will have a proven track record that demonstrates high-quality independent research although this is not a prerequisite. Here's more information about the openings and 2 NSF-funded projects. Feel free to reach out to me if you have any questions!

I am currently working on the following research topics:

Enhancing Doctor-Patient Communication Through Personal Health Data Sharing

People are tracking massive health data outside the clinic due to an explosion of wearable sensing and mobile health (mHealth) apps that support self-tracking. Although potential usefulness of self-tracking data is enormous, it is largely underutilized by patients and clinicians due to many obstacles, including difficulty in data sharing. This project is aiming at understanding patients’ and clinicians’ barriers toward personal health data sharing. This work is currently funded by NSF.

Semi-automated Tracking to Advance Personal Informatics

This research examines a novel self-tracking approach called semi-automated tracking to help people easily engage with a rich set of personal data, such as weight, activities, sleep pattern, and medication use. In principle, being aware of self-tracking data can help people reflect on their health condition and understand how their behavior affects their progress toward goals, making positive influence on people’s health and well-being. In practice, however, self-tracking is hard. Manual tracking approaches such as diaries require much effort, while automated tracking approaches such as wearable sensing tools significantly reduce the tracker’s awareness, accountability, and involvement achieved compared to when a person actively engages in manual tracking. To address these problems, this research proposes to design and develop a semi-automated tracking platform, combining both manual and automated data collection methods. This work is currently funded by NSF.

Intergenerational Collaborative Health Tracking

Personal health tracking has many benefits including increased health awareness, improved self-management behaviors, and informed decision-making. However, health tracking can be burdensome especially for elderly people. This project is aiming at investigating family-based collaboration as a strategy for increasing the utilization of health tracking technology by elderly people. We will leverage intergenerational relationships between elderly people and their adult children, emphasizing opportunities to enhance mutual awareness of health activities including sleep, exercise, diet, and medication adherence, making health more of a family-based joint project.

Selected Publications

Choe, E.K., Lee, B., Zhu, H., Riche, N.H., Baur, D. (2017).
Understanding Self-Reflection: How People Reflect on Personal Data Through Visual Data Exploration.
Proc. EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '17).
[PDF]
Zhu, H., Luo, Y., Choe, E.K. (2017).
Making Space for the Quality Care: Opportunities for Technology in Cognitive Behavioral Therapy for Insomnia.
Proc. ACM Human Factors in Computing Systems (CHI '17). [PDF]
Choe, E.K., Abdullah, S., Rabbi, M., Thomaz, E., Epstein, D.A., Kay, M., Cordeiro, F., Abowd, G.D., Choudhury, T., Fogarty, J., Lee, B., Matthews, M., Kientz., J.A. (2017).
Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications.
IEEE Pervasive Computing. [IEEE Link] [PDF]
Zhu, H., Colgan, J., Reddy, M., Choe, E.K. (2016).
Sharing Patient-Generated Data in Clinical Practices: An Interview Study.
Proc. American Medical Informatics Association (AMIA '16). [PDF]
Distinguished Paper Award Nominee.
Kim, Y., Jeon, J.H., Choe, E.K., Lee, B., Kim, K., Seo, J. (2016).
TimeAware: Leveraging Framing Effects to Enhance Personal Productivity.
Proc. ACM Human Factors in Computing Systems (CHI '16). [ACM] [PDF]
Choe, E.K., Lee, B., Kay, M., Pratt, W., Kientz, J.A. (2015).
SleepTight: Low-burden, Self-monitoring Technology for Capturing and Reflecting on Sleep Behaviors.
Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15) [PDF] [Presentation] [ACM]
Choe, E.K., Lee, B., schraefel, m.c. (2015).
Characterizing Visualization Insights from Quantified-Selfers' Personal Data Presentations.
IEEE Computer Graphics and Applications. [PDF] [IEEE Link]
Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A. (2014).
Understanding Quantified Selfers’ Practices in Collecting and Exploring Personal Data.
Proc. ACM Human Factors in Computing Systems (CHI '14).
[Acceptance rate 22.8%] Honorable Mention Award [PDF]