EDBT 2026 Demo / reviewers in the wild / expert
Qichen Liu
dblp:377/1723
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Personal fabrication and tangible interfaces · 50% Immersive interaction · 50% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Personal fabrication and tangible interfaces
tangible interaction |
1.0 | 1 | 2026 | Selecting Tangible Media for Immersive Exploration of Volumetric Scientific Data · CHI 2026 |
Visualization and visual analytics
scientific visualization |
0.3 | 1 | 2026 | Selecting Tangible Media for Immersive Exploration of Volumetric Scientific Data · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
design space · 2.0controlled user study · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Selecting Tangible Media for Immersive Exploration of Volumetric Scientific DataabstractImmersive scientific data exploration faces challenges in precise and efficient interaction. Tangible media offer a potential solution; but designers lack clear guidance on choosing the appropriate physical dimensionality (1D, 2D, or 3D) for different tasks. To address this problem, we present a design space structuring the relationship between the representative techniques on scientific data visualization and exploration, tangible interactions, and media dimensionality. We further developed a prototype to empirically explore these relationships according to our design space. In a controlled user study, we compared 1D, 2D, and 3D tangible media across seven core techniques. The results demonstrated that the 3D media (e.g., a box) were preferred when tasks required manipulating the entire volumetric data and acted as a proxy. Regarding the tasks requiring 2D operations or interior localization, the 2D media (e.g., a card) offered superior performance. For single-parameter techniques like histogram-based filtering, the 1D media (e.g., a pen) were overwhelmingly preferred for their simplicity and perceived ease of use. Zhouhao Wu, Huiting Kong, Mingming Zhou, Qichen Liu, Shuai Chen 0001, Chufan Lai, Richen Liu |
CHI | 4 |