特邀讲者：Koji Koyamada 演讲题目：Visual data science and its applications 报告摘要：Currently, we are focusing on the development of visual analytics systems which use interactive visualization technologies in order to draw a scientific discovery from big data from supercomputer, measurement systems and social networks. We are doing research on advanced visualization technologies which can effectively and efficiently process the big data. The fundamental visual data science is research on systems and related technologies for deriving new discoveries from big data by applying visualization technology. Currently, we are aiming at improving the visualization performance from a comprehensive viewpoint, a heuristic viewpoint, and an empathic viewpoint in each process of research question asking, hypothesis forming and testing, and social implementation that form the framework of scientific methods. In this talk, we will show our research accomplishments on fused volume visualization in the computational fluid dynamics, causality exploration in Life and Fishery Sciences. 个人简历：Prof. Koji Koyamada is currently a professor at the Academic Center for Computing and Media Studies, Kyoto University, Japan. His research interest includes modeling & simulation and visualization. He is a member of the Science Council of Japan, a former president of the Visualization Society Japan, and a former president of Japan Society of Simulation Technology. He received the IEMT/IMC outstanding paper award in 1998, the VSJ contribution award in 2009 and the VSJ outstanding paper award in 2010. He received his B.S., M.S. and Ph.D. degrees in electronic engineering from Kyoto University, Japan in 1983, 1985 and 1994, respectively, and worked for IBM Japan from 1985 to 1998. From 1998 to 2001 he was an associate professor at the Iwate Prefectural University, Japan. From 2001 to 2003, he was an associate professor at Kyoto University, Japan
报告摘要：Visualization provides essential accesses for users to comprehend such big data and gain insights, which is crucial for decision makers, political figures, as well as the general public. However, one major obsticle proventing the wider usage and application for visualization is the high learning curve for imelementing a visualization applicaiton or system. Although many system have been proposed to ease the usage of visualizaiton by providing quick prototyping or interactive visualization construction, there are strong need for more intelligence approaches to accelerate the construction pipeling of visualization. This talk will discuss a few appraoches we have taken to take the adanvagtes of machine learning for smart visualization. It is possible enable general public without any programing background to use or even construct visualization. 个人简介：Xiaoru Yuan is a tenured faculty member in the School of Electronics Engineering and Computer Science. He serviced as the vice director of Information Science Center, and Deputy vice director of National Engineering Laboratory on Big Data Analysis and Applications. 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 scientific visualization, information visualization and visual analytics. He has co-authored over 90 technical papers in IEEE Visualization, IEEE Information Visualization, IEEE TVCG, IEEE EuroVis, IEEE PacificVis 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 and his student team won awards over many times in IEEE VAST Challenges. He served on the program committees of IEEE VIS, EuroVis, and IEEE PacificVis. He was organization co-chair of IEEE PacificVis 2009, program chair of VINCI 2010, and poster chair of IEEE VIS 2015/2016 and paper chair of IEEE VIS 2017 and PacificVis 2015. He founded ChinaVis conference in 2014. He also serves on the editorial board of CCF journal of CAD&CG，Springer Journal of Visualization, and as guest editor of IEEE TVCG and IEEE CG&A. He is CCF outstanding member. He founded the visualization and visual analytics technical committee in Chinese Society of Image and Graphics (CSIG) and currently serve as the chair of the board. For more information, see http://vis.pku.edu.cn/wiki.