Bum Chul Kwon

TL;DR: A knowledge generation model for visual analytics is proposed that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes.

TL;DR: This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the users' trust building.

TL;DR: This study designs a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers, and demonstrates how it made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity.

TL;DR: This work systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement.

TL;DR: Clustervision is a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available and empowers users to choose an effective representation of their complex data.