Building on our award-winning methods for externalizing selection bias within the Tempo visual analytics system (which won Best Paper at ACM IUI), a follow-up article has now been published in ACM Transactions on Interactive Intelligent Systems. This collaborative project with the UNC Lineberger Comprehensive Cancer Center expanded on the original paper with more details and an initial evaluation with a use case and analysts from the UNC Lineberger Cancer Surveillance System (CIPHR, formerly known as ICISS).
This new article, titled “Adaptive Contextualization Methods for Combating Selection Bias during High-Dimensional Visualization,” is also our first article to be published on this topic since we were awarded a four-year NSF grant which is funding our efforts to further develop these contextual visualization techniques.
The new ACM TIIS article can be found here, and the full citation is as follows:
David Gotz, Shun Sun, Nan Cao, Rita Kundu, and Anne-Marie Meyer. Adaptive Contextualization Methods for Combating Selection Bias during High-Dimensional Visualization. ACM Transactions on Interactive Intelligent Systems (Volume 7, Issue 4, November 2017).