Category: Publications

We introduce VisPeerReview, a visualization-specific learning analytics dashboard (LAD) designed to scaffold peer feedback through an integrated visualization display, rubric-guided prompts, and inline annotation tools. We evaluated VisPeerReview through a three-phase mixed-methods study conducted in an undergraduate data visualization course, comparing it with Canvas’s default peer-review workflow.

We conducted a two-phase qualitative study: interviews with ten practitioners from diverse backgrounds revealed the heuristics they draw on when creating annotations, and interviews with seven visualization educators offered complementary perspectives situated within broader concerns of clarity, guidance, and viewer agency. These studies provide a systematic account of annotation design knowledge in professional settings, highlighting the considerations, trade-offs, and contextual judgments that shape the use of annotations.

We investigate whether users benefit from algorithmic choice when adapting line charts across screen sizes. In a within-subjects study (N=30), participants simplified nine datasets under three conditions: single pre-assigned technique (C1), multiple techniques (C2), and multiple techniques with manual point selection (C3), each with control over simplification level.

We conduct a between-subjects (N=40) observational study of representative data cleaning tasks performed with four tools spanning distinct interface paradigms: Jupyter (notebook), Excel (spreadsheet), ChatGPT (conversational AI), and OpenRefine (visual wranglers).

In this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data.

We introduce ChannelExplorer, an interactive visual analytics tool for analyzing image-based outputs across model layers, emphasizing data-driven insights over architecture analysis for exploring class separability.

We conducted a visual stenography task, where participants re-drew line charts to solicit information about the visual features they believed to be important. We systematically varied noise to observe how visual clutter influences which features people prioritize in their sketches. We identified three key strategies: Replicator, Trend Keeper, and De-noiser. Further, we found that participants tended to faithfully retain trends and peaks and valleys when these features were present, while periodicity and noise were represented semantically.

We present Buckaroo, a scalable visual data wrangling system that restructures data preparation as a direct manipulation task over visualizations. Buckaroo enables users to explore and repair data anomalies—such as missing values, outliers, and type mismatches—by interacting directly with coordinated data visualizations. The system extensibly supports user-defined error detectors and wranglers, tracks provenance for undo/redo, and generates reproducible scripts for downstream tasks. Buckaroo maintains efficient indexing data structures and differential storage to localize anomaly detection and minimize recomputation.

We conduct a user study comparing three alternatives-aggregated, trellis, and spiral line charts against standard line charts on tasks involving trend identification, making predictions, and decision-making. We found aggregated charts performed similarly to standard charts and support more accurate trend recognition and prediction; trellis and spiral charts generally lag. We also examined the impact on decision-making via a trust game. The results showed similar trust in standard and aggregated charts, varied trust in spiral charts, and a lean toward distrust in trellis charts. These findings provide guidance for practitioners choosing visualization strategies for dense temporal data.