I'm Christopher Du,
I seek to understand how users interact with and think about technology using mixed-methods research to create more usable and effective digital experiences.
I am a recent Master of Human-Computer Interaction graduate with experience formulating tractable research questions, conducting rigorous quantitative and qualitative studies, and translating findings into valuable insights and practical implications. More on my thesis.
My work spans user research, data analysis, usability testing, qualitative research, experiment and survey design, and prototype implementation.
More About Me
I was born and raised in the beautiful city of Toronto, Ontario. Scarborough more specifically, where, despite my qualms with its lack of urbanist planning, the best food in the city can be found! I also spent a few years in Ottawa, where I developed a love for effective vulnerable road user infrastructure and shawarma.
I have a life-long passion for video games, particularly story-rich RPGs and competitive FPS, shaped through childhood experiences with Dragon Age: Origins and Counter-Strike. Video games engendered a curiosity for understanding how the dynamic relationship between systems of varying complexity shape the overall experience for the user. I also have deep interests in music, film, and fantasy literature. And perhaps most important, I am an ardent supporter of Tottenham Hotspur FC.
Research Projects
Quantitative Research Inferential Statistics Qualitative Research User Interviews
Description:
Investigated the effects of three different augmented reality visual cue designs on user performance and usability in a guided piano-playing task. The primary goal was to better understand how increasingly complex visual cue design can affect future augmented reality-based music learning platforms. Our study provided insight into how complex visual cue design might be a negative addition for augmented reality-based music learning platforms.
- Co-authored poster based on project accepted for presentation at ACM Symposium on Spatial User Interaction (ACM SUI) 2026.
Role:
Designed the experiment of the study and the augmented reality visual cues, as well as carrying out the quantitative analysis and data visualizations.
Mixed Methods Inferential Statistics User Interviews Thematic Analysis
Description:
We looked at how three different text highlighting techniques can benefit text search under visual clutter in a virtual reality reading task. The research questions we posed were How does visual clutter affect visual text search in VR and can highlighting techniques help? Our study contributed to the understanding of how visual clutter affects users in VR and how these effects can be alleviated through the use of these highlighting techniques.
Role:
Co-designed semi-structured interview questions, carried out interviews with participants, and cleaned the transcripts for analysis. Lead the inductive thematic analysis of interview responses. Conducted statistical analysis of workload differences.
Descriptive Statistics Survey Design Usability Testing
Description:
I compared Spotify's Search and Browse library features to examine how each supports a common task involving music retrieval from the platform's library. The goal of this study was to determine how well the design of these features supported users' goals with the interface. This study confirmed expectations and discussed how the current design of these features under utilizes Browse by positioning it as an alternative to Search.
Role:
Designed and carried out the experiment, and conducted all statistical analyses.
Measuring Roadway Clutter in Driving Scenes
Mixed Methods Inferential Statistics Regression Analysis Mixed Effects Models Survey Design Thematic Analysis
Description:
A mixed-methods multi-study master's thesis centered on the concept of visual clutter in the driving environment, roadway clutter. I sought to determine how best to (1) define roadway clutter and (2) algorithmically measure it in driving scenes (i.e., videos and/or images of driving environments). I compared four algorithms through performance and subjective measures, and qualitative data. The ultimate goal was to provide a better systematic standard for the use, selection, and manipulation of driving scenes as an experimental variable for future HCI and human factors research (e.g., on car heads-up displays).
- Poster based on project presented at CARSP/ACIP 2026 Conference, awarded 3rd place prize in Student Poster Competition.
- Co-authored manuscript based on project submitted to a journal and currently in review.
Role:
Led the project with assistance from my thesis supervisor throughout.
Thoughts & Ramblings
View all posts »Something interested me so I looked at the data and talk about it a bit here.
