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.
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.