讲者：Koji Koyamada Kyoto University, professor
    演讲题目：Does visualization contribute to scientific discovery?
    报告摘要：In the open data era, it is an urgent task to foster data scientists that can utilize data mining, data analytics, deep learning and so on. Also, due to the advent of the artificial intelligence era, the role played by humans is questioned. In developing data scientists, the important foundation is a scientific method. In this talk, we would like to think about how visualization should be improved in order for human beings to properly utilize the scientific method to create scientific knowledge in the artificial intelligence era. Visualization is defined as a way to see the unseen. Also, in many definitions, data is an encoding of something, and information is data, which is recognized by humans. For human recognition, it is necessary to transfer data to the brain. Data visualization is to realize the information of data by utilizing the vision which has the largest band width to the brain. There is no objection to this visualization being a useful tool at the present time in many scientific research areas. In this talk, we would like to consider the role that visualization should play in order to create scientific knowledge with artificial intelligence.
    个人简历：He 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 an associate 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.

    讲者：袁晓如 北京大学，研究员
    演讲题目：社会媒体可视分析
    报告摘要：社会媒体一方面为人们提供便利，另一方面其产生数据的复杂性也为对其分析理解也带来了巨大的挑战。而可视化和可视分析利用人类视觉认知的高通量特点，通过图形和交互的形式表现信息的内在规律及其传递、表达的过程，充分结合人的智能和机器的计算分析能力，是人们理解复杂现象，诠释复杂数据的重要手段和途径。这里我们将选取典型社会媒体数据，探讨实际案例，分析可视化和可视分析对复杂数据理解的重要性，从数据规模，数据复杂性，任务复杂性，可扩展性等多个方面讨论在社会媒体数据可视化与可视分析面临的挑战和机遇。
    个人简介：袁晓如，北京大学"百人计划"研究员。现任职于北京大学机器感知与智能教育部重点实验室，信息科学技术学院信息科学中心副主任。1997/98年分获北京大学化学/知识产权专业双学位，2006年8月获美国明尼苏达大学计算机科学博士学位。主要研究方向包括科学可视化，信息可视化和可视分析等。在高维数据、时空轨迹数据、社会媒体数据、复杂流场数据等可视化与可视分析领域开展了系统的工作。在IEEE Visualization, IEEE Information Visualization, IEEE Visual Analytics Science and Technology (VAST), IEEE TVCG, IEEE EuroVis, IEEE PacificVis等重要国际可视化会议以及期刊上发表60余篇文章。关于高动态范围可视化的工作获得2005年 IEEE Visualization大会最佳应用论文奖。IEEE VIS可视化大会VAST Challenge 2013年竞赛网络安全数据可视分析部分获得Award of Outstanding Situation Awareness；2014年获VAST Challenge三项奖励。担任IEEE VIS，IEEE PacificVis等国际可视化会议程序委员会委员，2009年IEEE PacificVis组织委员会共同主席，2015年IEEE VIS Poster主席。创建了中国可视分析大会并担任2014年首届中国可视分析大会程序委员会共同主席，2015年大会主席。任《计算机辅助设计与图形学学报》，《数值计算与计算机应用》，Journal of Visualization (Springer)等国内外期刊编委，IEEE TVCG，IEEE CG&A 客座编辑。中国计算机学会(CCF)杰出会员。更多信息参见http://vis.pku.edu.cn/wiki