We are designing, developing, and evaluating a set of Contextual Visualization Methods for exploratory data analysis which are designed to support the discovery of more robust and generalizable insights from high-dimensional data. More specifically, this project is exploring new techniques for detecting, communicating, and reducing the impact of selection bias and other threats to validity which can arise during interactive visualization-based data analysis.
A more comprehensive summary of our work is provided in the project overview, and additional information can be found via the menu on the right side of this page.
This project supported in part by the National Science Foundation under Grant No. 1704018.