Minsuk Chang
My research field is Data Visualization and Human-Computer Interaction, understanding and supporting how humans perceive and interact with data. I try to combine cognitive science and computing to better support human-AI interaction.
I'm currently a 3rd-year PhD student advised by Cindy Xiong Bearfield. I've also interned at Tableau Research @ Salesforce
in Summer 2025, where I worked with Srishti Palani and Arjun Srinivasan. This summer, I'm interning at LG AI Research, working with Bumsoo Kang, Jiyong Cho, and Moontae Lee.
Research Interests
Interactive demos
News
- July 2026
Our paper on channel effectiveness and graphical perception was accepted to IEEE VIS 2026 as a full paper!
- July 2026
I gave an invited talk at the HCI Lab at Seoul National University.
- May 2026
Starting my internship at LG AI Research this summer.
- April 2026
I successfully passed my CS PhD qualifying exam!
- February 2026
The AI-Based Literacy Score Prediction Model is now open! Visit the Literacy Test page to check whether you are expert or novice in visualization literacy.
- January 2026
Our paper on decision-making and critical thinking was accepted to ACM IUI 2026! See you in Paphos!
- October 2025
Our paper on visualization literacy and visual attention was accepted to IEEE VIS 2025! See you all in Vienna!
Show earlier newsHide earlier news
- May 2025
Starting my Summer Research Internship at Tableau Research @ Salesforce.
- April 2025
Two papers accepted to EuroVis 2025 and VSS 2025.
- August 2024
Started my PhD journey at Georgia Tech, advised by Dr. Cindy Xiong Bearfield.
- February 2024
Our paper on assessing graphical perception was accepted to IEEE VIS 2024.
- January 2024
Paper on continual learning visualization accepted to IEEE PacificVis 2024.
Selected Publications

ACM IUI
•2026
Criticality: Scaffolding Decision-Making with Interactive Critical Thinking and Evidence-Based Reasoning Traces
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.


EuroVis
•2025
Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
We created a new annotation method where users can efficiently label the important area in a visualization.


IEEE VIS
•2024
Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness
We assessed how well image embedding models perceive visual encoding channels in charts, comparing their accuracy and discriminability against human graphical perception.

IEEE PacificVis
•2024
