Designers often create visualizations to achieve specific high-level analytical or
communication goals. These goals require people to naturally extract complex …

Visual clustering is a common perceptual task in scatterplots that supports diverse analytics
tasks (eg, cluster identification). However, even with the same scatterplot, the ways of …

Scatterplots commonly use color to encode categorical data. However, as datasets increase
in size and complexity, the efficacy of these channels may vary. Designers lack insight into …

Quality colormaps can help communicate important data patterns. However, finding an
aesthetically pleasing colormap that looks “just right” for a given scenario requires significant …

Data visualizations help extract insights from datasets, but reaching these insights requires
decomposing high level goals into low-level analytic tasks that can be complex due to …

Existing guidelines for categorical color selection are heuristic, often grounded in intuition
rather than empirical studies of readers' abilities. While design conventions recommend …

A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to
quantify how well labeled classes form compact, mutually separated clusters in the …

We propose a new approach to uncertainty communication: we keep the uncertainty
representation fixed, but adjust the distribution displayed to compensate for biases in …

Color restoration of ancient Chinese paintings plays a significant role in Chinese culture
protection and inheritance. However, traditional color restoration is challenging and time …

Shape is commonly used to distinguish between categories in multi-class scatterplots.
However, existing guidelines for choosing effective shape palettes rely largely on intuition …