I'm a Ph.D. student in Computer Science at The University of North Carolina at Chapel Hill, advised by Prof. David Gotz and Prof. Danielle Albers Szafir. I hope to build automated, accessible, and empirically grounded visual intelligence tools that can help advance exploratory data analysis and decision-making for general users.
Prior to that, I was a data visualization engineer at Ant Financial, working towards auto-BI systems and visualization & design grammars, e.g., AVA, G2, and Ant Design. I earned my M.Sc. and B.Eng. degrees from Shandong University, China.
Email: zeyuwang AT cs.unc.edu
Research
I'm mainly interested in exploring the cognitive and perceptual impact to motivate empirically grounded visualization system design, thus helping people infer insights from data. Prior research can be found here.
Striking the Right Balance: Systematic Assessment of Evaluation Method Distribution Across Contribution Types
F. Lin, A. Z. Wang, M. D. Rahman, D. Albers Szafir, and G. J. Quadri.
Evaluation and Beyond — Methodological Approaches for Visualization (BELIV), 2024.
Our Stories, Our Data: Co-designing Visualizations with People with Intellectual and Developmental Disabilities
K. Wu, G. J. Quadri, A. Z. Wang, D. K. Osei-Tutu, E. Peterson, V. Koushik, and D. Albers Szafir.
ACM SIGACCESS Computers and Accessibility (ASSETS), 2024.
Causal Priors and Their Influence on Judgements of Causality in Visualized Data
A. Z. Wang, D. Borland, T. C. Peck, W. Wang, and D. Gotz.
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS 2024), 2025.
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis
A. Z. Wang, D. Borland, and D. Gotz.
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS 2024), 2025. Best Paper Honorable Mention
Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes
C. Tseng, A. Z. Wang, G. J. Quadri, and D. Albers Szafir.
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS 2024), 2025.
A Framework to Improve Causal Inferences from Visualizations Using Counterfactual Operators
A. Z. Wang, D. Borland, and D. Gotz.
Information Visualization, 2024.
Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
C. Tseng, A. Z. Wang, G. J. Quadri, and D. Albers Szafir.
EG/IEEE Conference on Visualization (EuroVis), 2024. Best Short Paper
Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension
G. J. Quadri, A. Z. Wang, Z. Wang, J. Adorno, P. Rosen, and D. Albers Szafir.
ACM Human Factors in Computing Systems (CHI), 2024.
An Empirical Study of Counterfactual Visualization to Support Visual Causal Inference
A. Z. Wang, D. Borland, and D. Gotz.
Information Visualization, 2024.
Using Counterfactuals to Improve Causal Inferences from Visualizations
D. Borland, A. Z. Wang, and D. Gotz.
IEEE Computer Graphics and Applications (CG&A), 2024.
Measuring Categorical Perception in Color-Coded Scatterplots
C. Tseng, G. J. Quadri, Z. Wang, and D. Albers Szafir.
ACM Human Factors in Computing Systems (CHI), 2023.