38 citations · 165 across the 25 of their papers we have counts for
5 papers · 1 filter
Analysis Without Data: Teaching Students to Tackle the VAST Challenge
Edward W He, Daniel Tolessa, Ashley Suh +1
The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development pr…
PIXAL: Anomaly Reasoning with Visual Analytics
Brian Montambault, Camelia D. Brumar, Michael Behrisch +1
Anomaly detection remains an open challenge in many application areas. While there are a number of available machine learning algorithms for detecting anomalies, analysts are frequ…
Inferential Tasks as an Evaluation Technique for Visualization
Ashley Suh, Ab Mosca, Shannon Robinson +4
Designing suitable tasks for visualization evaluation remains challenging. Traditional evaluation techniques commonly rely on 'low-level' or 'open-ended' tasks to assess the effica…
Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts
Ashley Suh, Gabriel Appleby, Erik W. Anderson +3
Presenting a predictive model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metri…
A Grammar of Hypotheses for Visualization, Data, and Analysis
Ashley Suh, Ab Mosca, Eugene Wu +1
We present a grammar for expressing hypotheses in visual data analysis to formalize the previously abstract notion of "analysis tasks." Through the lens of our grammar, we lay the…