Qichen Liu

dblp:377/1723 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Personal fabrication and tangible interfaces
tangible interaction
1.012026
Selecting Tangible Media for Immersive Exploration of Volumetric Scientific Data · CHI 2026
Visualization and visual analytics
scientific visualization
0.312026
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
YearPublicationVenuePosition
2026 Selecting Tangible Media for Immersive Exploration of Volumetric Scientific Data
abstract
Immersive 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
CHI4