Smooth Interactive Visualization

This research examines the use of signal processing theory to improve the smoothness of interactive visualization, producing visualizations that are more beautiful and more rapidly interactive, even for big data.

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Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills

Self JZ, Self N, House L, Evia JR, Leman S, North C. Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills. In: Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference. Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference. ; 2017. p. 10.

Observation-Level Interaction with Clustering and Dimension Reduction Algorithms

Wenskovitch J, North C. Observation-Level Interaction with Clustering and Dimension Reduction Algorithms. In: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. New York, NY, USA; 2017. p. 14:1–14:6. Available from: http://doi.acm.org/10.1145/3077257.3077259

Caleb Reach graduates with PhD, accepts position at Google NYC

Caleb Reach successfully completed and defended his dissertation, entitled "Smooth Interactive Visualization". His dissertation developed a formal methodology for smoothness in interactive visualization based on signal processing theory. Congratulations, Caleb!
https://vtechworks.lib.vt.edu/handle/10919/78848

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