Marrying Data and User with Interactive Visualization

Abstract:
Recently, the amount of digital information we want to explore is growing at a tremendous rate. The challenge of transforming such large, heterogeneous, and complex data into useful knowledge calls for novel computational and perceptual aids. My research employs an interactive and visual approach to facilitate users with the discovery and communication of insights in data. In this talk, I will discuss the phenomena that there exists a huge divide between the raw data and the user who wants to gain knowledge from it. I will then present a subset of my work on tackling this problem by leveraging highly interactive visualization techniques. The design studies of those techniques have touched a range of data formats and application domains, including time-series analysis, social media mining, and computational linguistics. I will also outline a few future research directions to empower the user with effective visualizations.

Short-Bio:
Jian Zhao is a Ph.D. candidate at the Department of Computer Science, University of Toronto, where he works with Dr. Ravin Balakrishnan. His research interests, generally, are related to the areas of Visual Analytics, Information Visualization, and Human-Computer Interaction. He is the recipient of a Wolfond Scholarship, a Wolfond Fellowship, a Robert E. Lansdale/Okino Graduate Fellowship, and an Honorable Mention paper award at IEEE VAST. He is a winner of the Yelp Dataset Challenge Grand Prize. In addition to working at University of Toronto, he collaborates with researchers from leading industrial labs during internships at Microsoft Research, IBM Research, and Adobe Research. Before joining University of Toronto, he received his B.Eng. degree in Computer Science at Zhejiang University in China. More information can be found on his personal homepage: www.cs.toronto.edu/~jianzhao/