Task-driven Automated Data Visualization

Overview

Abstract

By providing visual representations of data, visualization can help people to carry out many data analysis tasks more effectively. Given a data set, however, there are often too many possible visualization techniques, with each technique having many parameters to be tuned. This arises the question is if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. With my group, I developed a number of techniques to achieve this goal for different data sets such as time-series data, high-dimensional data, text as well as graphs. Among them, our text visualization tools EdWordle and ShapeWordle have attracted thousands of internet users.

Brief Biography

Yunhai Wang is a Professor at Shandong University, China. He received his Ph.D. in 2011 from the University of the Chinese Academy of Sciences (UCAS), China. Prior to joining Shandong University, he was an associate researcher at the Shenzhen Institutes of Advanced Technology (SIAT), China. His research interests include scientific and information visualization with a focus on automated data visualization. He has published more than 30 papers in top venues for visualization and computer graphics, such as IEEE VIS, IEEE TVCG, ACM SIGGRAPH/SIGGRAPH Asia, ACM TOG and received 13 patents. He has collaborated with many international researchers and is the main organizer of the yearly Sino-German Visualization Workshop.