“Visualizing the COVID-19 Pandemic”Professor Xiaoru Yuan, School of AI, Peking University, China |
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Bio
Xiaoru Yuan is a professor in the School of AI at Peking University. He currently serves as the deputy vice director of National Engineering Laboratory on Big Data Analysis and Applications and vice director of Key Laboratory of Machine Perception (MOE), at Peking University. He received a Ph.D. degree in computer science in 2006, from the University of Minnesota at Twin Cities. His primary research interests are in the field of visualization and visual analytics. His co-authored paper on high dynamic range volume visualization received Best Application Paper Award at the IEEE Visualization 2005 conference. He and his student team won awards multiple times in IEEE VAST Challenges. He received the Okawa Research Fund Award in 2018.
He served on the program and conference committees of IEEE VIS, ACM CHI, EuroVis, and IEEE PacificVis. He was organization co-chair of IEEE PacificVis 2009, poster chair of IEEE VIS 2015/2016, paper chair of IEEE VIS 2017 and PacificVis 2015, and IEEE VIS 2021 area paper chair. He is on the editor board of IEEE TVCG, Journal of Visualization and several other international journals. He initiated and co-founded ChinaVis conference in 2014. He is the director of the visualization and visual analytics technical committee at China Society of Image and Graphics.
Abstract
During the still ongoing COVID-19 pandemic, visualization has demonstrated its unique power in communication of the information era. In this talk, I will first present a series of visualizations done by my team and collaborators in the beginning of 2020 on the theme of COVID-19. Among them, some visualizations have been broadcasted by the media with a large audience. Then a visual analytical platform for collaboratively collecting, annotating, and analyzing a global collection of visualizations related to the COVID-19 pandemic will be introduced. The visualization samples we have collected through this platform could inspire further research in the direction of understanding how visualizations interact with the society. Key findings from this systematic approach will be discussed.