I am an assistant professor at Penn State's College of Information Sciences and Technology (IST). I run Design Square, a group of researchers and students at Penn State's IST studying Human Computer Interaction (HCI) and Health Informatics.

With a background in HCI, Health Informatics, and Industrial Design, I study ways to design technology to help people become empowered individuals 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. My dissertation is titled, "Designing Self-Monitoring Technology to Promote Data Capture and Reflection."

I received my 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. If you want to learn more about my group, visit this page.

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. Before you contact me, click here to learn more.

I am organizing a workshop, "Leveraging Patient-Generated Data for Collaborative Decision Making in Healthcare" at PervasiveHealth 2017 (May 23, 2017 Barcelona, Spain).

If your work deals with interesting challenges in patient-generated data (PGD) & visualization & data sharing, we invite you to submit a position paper to our workshop. Deadline is March 20, 2017. Please visit the workshop website.

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 and Penn State IST's Seed Grant.

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.

Visualized Self

We are designing a personal data platform called "Visualized Self" to help people fully leverage their personal data to empower themselves. Visualized Self supports the full spectrum of self-monitoring including data collection, analysis, and sharing. We are aiming to help laypeople engage with and gain insights from their data. This work is currently funded by Microsoft Research.

Nudging by Design

We are exploring ways to create persuasive, effective feedback to nudge people toward positive behaviors—such as making healthy decisions [AMIA 2013] and privacy-preserving choices [Interact 2013]. We are providing empirical guidance for creating persuasive feedback, thereby helping people design applications to promote positive behaviors.

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).
To appear. [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). To appear.
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]
Zhu, H., Colgan, J., Reddy, M., Choe, E.K. (2016).
Sharing Patient-Generated Data in Clinical Practice: An Interview Study.
Proc. American Medical Informatics Association (AMIA '16). [PDF]
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]