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Scalable Data Visualization Systems for Broad Audiences
Leo Zhicheng Liu
Virtual-https://umd.zoom.us/j/93637673064?pwd=TzJYcE15UXg0MTJSQXJ5UFFLMlBNZz09
Friday, October 16, 2020, 11:00 am-12:00 pm Calendar
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Abstract

Two recent trends have brought opportunities and challenges in data visualization research. First, data are increasing in size and complexity across problem domains, demanding interpretable and performant visualization systems. In addition, more diverse users, such as scientists, analysts, journalists, and designers, need to work with data as an integral part of their jobs. They require visualization tools that offer low barriers of entry without sacrificing analytic or expressive power. I will present research projects that address these two challenges from a human-centered perspective. To support exploratory analysis of large multivariate and event sequence datasets, I use perceptual and interactive scalability as the driving principle to propose new interaction techniques. To make visualization tools accessible to a broader range of users, novel visualization process models can power the design and construction of natural language interfaces and visualization authoring systems.

Bio

Zhicheng "Leo" Liu is an assistant professor in the department of computer science at University of Maryland. Prior to joining UMD in August 2020, he was a research scientist at the Creative Intelligence Lab, Adobe Research, where he worked on data visualization and human-computer interaction. Liu received his PhD in the Human-Centered Computing program from Georgia Tech and was a postdoctoral fellow at the Department of Computer Science of Stanford University. His research has received two Best Paper Awards at ACM CHI, a Test of Time Award at IEEE VAST, multiple Best Paper Honorable Mentions at IEEE InfoVis, IEEE VAST and ACM CHI, and dissertation awards from Georgia Tech and the IEEE Visualization and Graphics Technical Committee.

This talk is organized by Richa Mathur