I am an associate professor at University of Maryland's College of Information Studies (a.k.a. "iSchool"). I also serve as the Director of the Doctoral Program at the iSchool.

My research bridges the fields of Human-Computer Interaction (HCI), Health Informatics, and Ubiquitous Computing. With an overarching goal of empowering individuals, my research centers on examining major challenges people face in leveraging personal data, such as personal data collection and exploration. More recently, I have been exploring multimodal interaction as a means to collect rich personal data, promote reflection, and help people dive into their data. My work has been funded by the National Science Foundation, National Institutes of Health, and Microsoft Research. I have been serving on the editorial boards of PACM IMWUT and Foundations and Trends in Human-Computer Interaction. 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.

[CV] [Google Scholar].

Information for 2021-2022 PhD Applicants

I will be accepting PhD students from both Information Studies and Computer Science. My research group envisions new ways of designing technologies that are inclusive of and beneficial to people with diverse tracking needs. More recently, I have been working with and for older adults, children, stroke patients, and visually impaired individuals to design personal informatics systems to help them gain new experiences interacting with their personal data and achieve individual goals. If you are interested in these topics, apply to the Ph.D. program in the College of Information Studies (Deadline: 12/10) or in the Department of Computer Science (Deadline: 12/17). If you include my name in your SOP, I will most likely review your application, which I will read with great interest. I look forward to learning about your exciting research ideas and vision.

My ongoing research projects

OmniTrack: 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. We designed a mobile self-tracking platform called OmniTrack, which helps people design and build trackers based on their diverse tracking needs without programming. This tool can help various stakeholders including Quantified-Selfers who have unique tracking needs, as well as researchers who want to incorporate in-situ data in their research. If you are a researcher wanting to incorporate trackers, to run Ecological Momentary Assessment studies, or to conduct mobile diary studies, please contact us! This work is currently funded by NSF.

Related publications:

Luo, Y., Liu, P., Choe, E.K. (2019).
Co-Designing Food Trackers with Dietitians: Identifying Design Opportunities for Food Tracker Customization.
Proc. ACM Human Factors in Computing Systems (CHI '19).
[Acceptance rate 23.8%] [PDF]
Kim, Y-H., Choe, E.K., Lee, B., Seo, J. (2019).
Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity.
Proc. ACM Human Factors in Computing Systems (CHI '19).
[Acceptance rate 23.8%] [PDF]
Kim, Y-H., Jeon, J.H., Lee, B., Choe, E.K., Seo, J. (2017).
OmniTrack: A Flexible Self-Tracking Approach Leveraging Semi-Automated Tracking.
Proc. ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). [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]

Personal Data Visualization & Mobile Data Visualization

What do you do once you have your own tracking regimen and have collected rich data about yourself? In my work, I examine ways to support self-trackers in leveraging their data through visualizations. In the personal data context, visualizations can help people identify insights, make decisions and act on the insight, and communicate and share insights with others. The personal data context also poses an interesting challenge, that is, the mobile context (i.e., small screen) where people often consume personal data. Despite the increased prevalence of visualization on mobile devices, we are missing a consolidated set of best practices and ways to evaluate mobile data visualization.

To help people gain rich insights through visualizations, we need to understand the types of insight that people value and the contexts (or use cases) of how people consume personal data. In my work, I conduct empirical studies to understand how people reflect on their personal data and what insights they gain from visual exploration with personal data. To support richer personal data exploration on mobile devices, I design novel ways to help people explore their personal data on mobile devices (smartphones, tablets, and smartwatches) by leveraging multimodal interaction and provide best practice examples.

Related publications & organized workshop:

Choe, E.K., Dachselt, R., Isenberg, P., Lee, B. (2019).
Mobile Data Visualization.
Dagstuhl Seminar. July 14–19, 2019. https://www.dagstuhl.de/19292
Brehmer, M., Lee, B., Isenberg, P., Choe, E.K. (2019).
Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation.
IEEE TVCG (InfoVis 2018) 25(1): 619–629. [IEEE Link] [PDF][Talk]
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).
[Acceptance Rate: 24%] [PDF] [Presentation]
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]

Personal Productivity & Healthy Work Habit

What does "productivity" mean for individuals? How to help people be productive and healthy in their work and life? How to help information workers take regular breaks during work to reduce sedentary behaviors? I have been exploring these topics from a Personal Informatics angle, and here are some of my previous and ongoing attempts to understand the nature of productivity at workplace and ways to create a healthy work habit:

Kim, Y-H., Choe, E.K., Lee, B., Seo, J. (2019).
Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity.
Proc. ACM Human Factors in Computing Systems (CHI '19).
[Acceptance rate 23.8%] [PDF]
Luo, Y., Lee, B., Wohn, D.Y., Rebar, A.L., Conroy, D.E., Choe, E.K. (2018).
Time for Break: Understanding Information Workers’ Sedentary Behavior Through a Break Prompting System.
Proc. ACM Human Factors in Computing Systems (CHI '18).
[Acceptance Rate: 25.7%] [PDF]
Kim, Y-H., 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).
[Acceptance Rate: 23%] [ACM] [PDF]