EDBT 2026 Demo / reviewers in the wild / expert
Ziqin Gao
dblp:389/0179
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
biomedical visualization |
0.8 | 1 | 2024 | Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics › biological data visualization
neuron tracing |
0.8 | 1 | 2024 | Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024 |
Immersive interaction › extended reality
extended reality interaction |
0.8 | 1 | 2024 | Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics
volume visualization |
0.2 | 1 | 2024 | Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024 |
Methods — techniques the papers use, named apart from their topics
interactors · 1.5extended reality · 1.5GPU-accelerated tracing · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Alleviating subgraph-induced oversmoothing in link prediction via coarse graining
Dong Hao, Ziqin Gao, Liming Pan |
Neurocomputing | 3 |
| 2024 | Efficient and Accurate Semi-Automatic Neuron Tracing with Extended RealityabstractNeuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving accurate neuron tracing is critical for profiling the neuroanatomical structure at single-cell level and analyzing the neuronal circuits and projections at whole-brain scale. However, the process often demands substantial human involvement and represents a nontrivial task. Conventional solutions towards neuron tracing often contend with challenges such as non-intuitive user interactions, suboptimal data generation throughput, and ambiguous visualization. In this paper, we introduce a novel method that leverages the power of extended reality (XR) for intuitive and progressive semi-automatic neuron tracing in real time. In our method, we have defined a set of interactors for controllable and efficient interactions for neuron tracing in an immersive environment. We have also developed a GPU-accelerated automatic tracing algorithm that can generate updated neuron reconstruction in real time. In addition, we have built a visualizer for fast and improved visual experience, particularly when working with both volumetric images and 3D objects. Our method has been successfully implemented with one virtual reality (VR) headset and one augmented reality (AR) headset with satisfying results achieved. We also conducted two user studies and proved the effectiveness of the interactors and the efficiency of our method in comparison with other approaches for neuron tracing. Zexin Yuan, Jiaqi Xi, Ziqin Gao, Ying Li 0028, Xiaoqiang Zhu, Yun Stone Shi, Frank Guan |
IEEE Trans. Vis. Comput. Graph. | 4 |