VLDB 2026 Research / reviewers in the wild / expert

Robert Gaschler

dblp:165/7276 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
3since 2021 · last 2023
0000-0002-8576-5330ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 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
3 papers
Visualization and visual analytics · 67% Geometric modeling and processing · 33%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
model fitting		 1.222023
Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models · IEEE Trans. Vis. Comput. Graph. 2023
Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise · IEEE Trans. Vis. Comput. Graph. 2021
Visualization and visual analytics
scatterplot		 1.222023
Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models · IEEE Trans. Vis. Comput. Graph. 2023
Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise · IEEE Trans. Vis. Comput. Graph. 2021
Visualization and visual analytics › graph visualization
node-link diagram		 0.712023
Color-Encoded Links Improve Homophily Perception in Node-Link Diagrams · IEEE Trans. Vis. Comput. Graph. 2023
Visualization and visual analytics
graphical perception		 0.322023
Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models · IEEE Trans. Vis. Comput. Graph. 2023
Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise · IEEE Trans. Vis. Comput. Graph. 2021
Visualization and visual analytics
graph visualization		 0.212023
Color-Encoded Links Improve Homophily Perception in Node-Link Diagrams · IEEE Trans. Vis. Comput. Graph. 2023

Methods — techniques the papers use, named apart from their topics

empirical study · 1.2online experiment · 0.7
YearPublicationVenuePosition
2023 Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models
abstract
Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ("lollipops") supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).
Daniel Reimann, Nilam Ram, Robert Gaschler
 IEEE Trans. Vis. Comput. Graph.3
2023 Color-Encoded Links Improve Homophily Perception in Node-Link Diagrams
abstract
Node-link diagrams enable visual assessment of homophily when viewers can identify and evaluate the relative number of intra-cluster and inter-cluster links. Our online experiment shows that a new design with link type encoded edge color leads to more accurate perception of homophily than a design with same-color edges.
Daniel Reimann, André Schulz 0001, Nilam Ram, Robert Gaschler
 IEEE Trans. Vis. Comput. Graph.4
2021 Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise
abstract
Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.
Daniel Reimann, Christine Blech, Nilam Ram, Robert Gaschler
 IEEE Trans. Vis. Comput. Graph.4