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Yuan, Xiaoru

Author:   Date:2019-04-13   ClickTimes:

Yuan, Xiaoru

Associate Professor

Research Interests: Visualization, visual analytics

Office Phone: 86-10-6276 7658

Email: xiaoru.yuan@pku.edu.cn

Yuan, Xiaoru is a tenured associate professor in the Department of Machine Intelligence, School of EECS. He services as the vice director of Information Science Center, at Peking University. He received Bachelor degrees in chemistry and law from Peking University, China, in 1997 and 1998, respectively. He received the 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.

He has co-authored over 70 technical papers in IEEE VIS/TVCG/PacificVis, EuroVis and other major international visualization conference and journals. His co-authored work on high dynamic range volume visualization received Best Application Paper Award at the IEEE Visualization 2005 conference. He led his student team won six awards in IEEE VAST Challenges. He served on the program committees of IEEE VIS and many other international conferences. He was organization co-chair of IEEE PacificVis 2009, program chair of VINCI 2010, paper chair of IEEE VIS 2017, IEEE PacificVis 2016, poster co-chair of IEEE VIS 2015 and 2016. He co-founded the ChinaVis conference since 2014 and served as the conference Chair on 2015. He also served on the editorial board of CCF journal of CAD&CG and Springer Journal of Visualization, Journal of Visual Language and Computing, and as guest editor of IEEE TVCG and IEEE CG&A.

His Major research contribution includes:

1) Scalable Large Scale Scientific Visualization: Scientists are generating ever-growing scale of data with supercomputers in this big data era. Visualization has been increasingly important for analyzing, understanding, and revealing insights in data. Our research interests generally involve in scalable visualization and analysis of large scientific data. We largely employ and develop parallel and scalable methods to visualize and analyze data on cutting-edge HPC platforms. Feature scalability. We meet the challenge of the new data and needs in scientific research, e.g. increasing number of variables and ensemble constituents, coupled analysis of scalar and vector attributes, etc. Our research focus on multivariate, multi-valued, and ensemble flow data, which consists of both scalar and vector attributes in the context of our studies. Alternative to traditional flow visualization methods which have been studied for decades, we emphasize on the scalable analysis of indirect and multi-faceted features. We also extensively use both Eulerian and Lagrangian method for the flow data analysis from multiple perspectives. More recent work in this direction involves developing new approaches to take the advantage of data access pattern to reduce the I/O cost and improve the performance in load balance.

2) High-dimensional data Visualization: High-dimensional data refers to data items with multiple attributes. The key task is to simultaneously visualize and relate multiple attributes, in order to reveal data patterns as well as attribute relationships. Many visualization techniques have been proposed to fulfill the task, such as scatterplot matrix, parallel coordinates, etc. Yet, these are not perfect solutions, not to mention the many unexploited research problems and application fields. Regarding all the potentialities, we start the series of high-dimensional visualization researches in the PKU Vis lab. We dedicate to improve traditional techniques, propose new methods, and explore untouched application fields.

3) Urban Visualization: We focus on trajectory data. Effective visualization and analysis of such data may help us better understand the urban transportation system, find out strategies to reduce the number of accidents and traffic jams, thus ensuring people's safety, facilitating people's trip and elevating the economic efficiency. In a series of projects, we studied several aspects with trajectory data, including: traffic density, traffic jams and traffic pattern correlation.

4) New visualization methods: In this direction, we developed series of approaches of

a) Map metaphor to visualize information diffusion and event development on social media. We developed methods called D-Map and E-Map to achieve such goals.

b) Novel visualization authoring tools. One major obstacle to prevent the wide usage of visualization by public users are the high learning curve of coding. We developed a few systems to construct new visualization (iVisDesigner) or argument new interaction on existing visualization (Interaction+) without coding efforts.

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