Publications
Below are my published research papers from peer-reviewed conferences and workshops. For a complete list, please visit my Google Scholar profile.
2026
3 papersIEEE VIS 2026
Revisiting Channel Effectiveness: A Multi-Dimensional Evaluation with Primitive Visual Stimuli
S. Lee, S. Park, M. Chang, J. Seo
We revisit the effectiveness of visual encoding channels through a multi-dimensional evaluation using primitive visual stimuli.
ACM IUI 2026
Criticality: Scaffolding Decision-Making with Interactive Critical Thinking and Evidence-Based Reasoning Traces
M. Chang, A. Srinivasan, S. Palani
We introduce Criticality, a system that operationalizes the Paul-Elder Critical Thinking framework to structure reasoning into interactive elements and grounds claims in verifiable evidence.
Paphos, Cyprus
IEEE PacificVis 2026
beautiVis: An Annotated Visualization Dataset from Reddit's r/dataisbeautiful
K. Lin, S. S. Ru, M. Chang, C. X. Bearfield
We built an annotated dataset of real-world visualizations collected from Reddit's r/dataisbeautiful community.
Sydney, Australia
2025
3 papersIEEE VIS 2025
Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention
M. Chang, Y. Wang, H. W. Wang, Y. Zhou, A. Bulling, C. X. Bearfield
We trained a bidirectional prediction model that predicts visualization literacy based on visual attention and vice versa.
Vienna, Austria · Acceptance rate 24.3% (131/537)
EuroVis 2025
Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
M. Chang, Y. Wang, H. W. Wang, A. Bulling, C. X. Bearfield
We created a new annotation method where users can efficiently label the important area in a visualization.
Luxembourg, Luxembourg · Acceptance rate 33% (20/60)
2024
2 papersIEEE VIS 2024
Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness
S. Lee, M. Chang, S. Park, J. Seo
We assessed how well image embedding models perceive visual encoding channels in charts, comparing their accuracy and discriminability against human graphical perception.
Florida, USA · Acceptance rate 31.9% (66/207)
IEEE PacificVis 2024
CLeVer: Continual Learning Visualizer for Detecting Task Transition Failure
M. Chang, D. Kim, H. Jeon, S. Jung, J. Seo
Tokyo, Japan
2023
1 paperIEEE BigData 2023
Tensor Space Model-based Textual Data Augmentation for Text Classification
M. Chang, H.-J. Kim
Sorrento, Italy