Graduate mentor's supervisor: Prof. Jian Zhao
Every day, we encounter more information than we can process in the form of news feeds, research papers, dashboards, or documentation. The standard response is either to compress everything into a summary (losing important detail) or to present everything at once (overwhelming the reader). Neither works well.
This project asks: can we use visual design to let people navigate information at their own depth? The core idea is progressive disclosure through visual cues. Specifically, using symbols, icons, and glyphs to signal that more detail exists, and revealing that detail only when someone expresses interest (by clicking, hovering, or zooming in). Think of it like a map: at a distance, you see city names; as you zoom in, streets appear; closer still, individual buildings. We want to apply that same logic to arbitrary information.
The broader impact is significant. If we can identify visual design principles that reliably support this kind of layered navigation, those principles could apply anywhere information overload is a problem: scientific communication, data dashboards, educational interfaces, and more.
Short-term goals (within the term):
- Conduct a focused literature review on progressive disclosure, semantic zooming, and visual encoding (icons, glyphs, symbols)
- Prototype a simple browser-based interface that demonstrates layered information reveal using basic visual cues
- Run informal user tests with peers to gather early feedback on which visual signals are intuitive
Medium-term/Long-term goals (students who continue beyond the program):
- Refine the visual encoding vocabulary: which glyphs or symbols best communicate "there is more here"?
- Expand the prototype to handle richer content types (e.g., structured text, images, hierarchical data)
- Design and run a more formal study comparing the layered approach against a standard flat presentation
- Develop design guidelines for visual progressive disclosure that generalize across domains
- Explore integration with generative AI to automate the transformation of dense content into layered visual representations
The team of 3–4 students can divide naturally: one or two students driving the prototype, one leading the literature synthesis, and one coordinating user testing and documenting findings with overlap and collaboration throughout. The project is intentionally structured so that different tasks suit different strengths: students comfortable with code can focus on building and prototyping; students with a design sensibility can focus on visual encoding and user-facing decisions; students who enjoy reading and synthesis can lead the literature review strand.
For this project, we are looking for students who have completed at least their second year, have exposure to programming fundamentals, and have some interest in design, human-computer interaction, or data. Specific experience with visualization or HCI is not required.