特邀讲者(排名不分先后)

马匡六

马匡六 美国加利福尼州大学Davis分校
Kwan-Liu Ma教授, IEEE Fellow (2012), 现任教于美国加利福尼州大学Davis分校,UC Davis Center for Visualization主任,领导着VIDI (Visualization and Interface Design Innovation)研究组,和美国能源部的SciDAC Institute for Ultra-Scale Visualization (2006-2012)。他的研究领域包括可视化、高性能计算以及用户界面设计。他于1993年在Utah大学获得计算机博士学位,1993到1999年,在ICASE/NASA LaRC担任研究员,1999年加入美国加利福尼州大学Davis分校。由于在并行可视化方面的贡献,马匡六教授次年荣获美国青年科学家最高荣誉 - 美国青年科学家及工程师总统奖(PECASE)。 2007年,获得大学工学院Outstanding Mid-Career Research Faculty Award。2013获得IEEE Visualization Technical Achievement Award。 马匡六教授领导着一支包括25名研究人员的队伍,在大规模数据可视化、信息可视化、可视界面设计、艺术绘制和体可视化方面做出了卓越的成就。近年来,他组织了2005年 NSF Workshop on Cyber Security, 2006年起的Workshop on Ultra-Scale Visualization, 2007年 Asia-Pacific Symposium on Visualization, 2008年 Pacific Visualization Symposium, 以及 2008年 Eurographics Symposium on Parallel Graphics and Visualization。他还担任2008,2009年 IEEE Visualization会议论文主席。马匡六教授同时是IEEE Computer Graphics and Applications副主编和IEEE Transactions on Visualization and Graphics 的编委 (2007-2011)。

Keynote: Big-Data Visualization Techniques for Studying Behaviors, Connections, and Evolution

Abstract: The widespread use of Internet has led to information explosion, a daunting challenge we must address in order to make sense and maximize utilization of the available information. Visualization proves effective in uncovering the hidden structures and patterns in massive, dynamically changing information spaces. I will present techniques that my group at UC Davis has developed for visualizing and reasoning the behaviors of individuals or groups, the connections between them, and their evolutions.

Hans-Christian Hege

Hans-Christian Hege Zuse Institute Berlin
Hans-Christian Hege is head of the Visualization and Data Analysis Department at Zuse Institute Berlin (ZIB). After studying physics and mathematics he joined ZIB and started in 1991 the visualization department – performing research in visual data analysis and developing visualization software, such as Amira and Biosphere3D. He taught as a guest professor at Universitat Pompeu Fabra, Barcelona, and as honorary professor at the German Film School (University for Digital Media Production). His research interests include visual computing as well as applications in life sciences, natural sciences and engineering. He co-founded the book series Mathematics + Visualization (Springer) and co-chaired Eurographics Medical Prize Competition – EG MedPrize 2013, Visualization in Medicine and Life Sciences – VMLS 2013 and 2009, EG/IEEE Symposium on Visualization – EuroVis 2009, IEEE/EG International Symposia on Volume Graphics – VG 2007 and 2008, Topology-Based Methods in Visualization – TopoInVis 2007, Visualization and Mathematics – VisMath 2002, 1997 and 1995, and the VideoMath Festival at the International Congress of Mathematicians (ICM) 1998. He also co-founded the companies Mental Images (1986), Indeed-Visual Concepts (1999) – now Visage Imaging –, and Lenné3D (2005).

Keynote: What is the Shape of a Molecule?

Abstract: Molecular visualization has become an indispensable part of molecular analysis and represents one of the particularly successful branches of data visualization. Various graphical representations of molecules have been invented and serve different needs in molecular sciences.
Are these representations suited, if one is aiming at visual analysis, i.e. if one wants to draw physically correct conclusions from molecular depictions? Important biophysical phenomena, like the transport of molecules through a biological membrane, often depend on fine details. It turns out that commonly used types of molecular depictions sometimes do not allow us to draw correct conclusions.
When trying to depict molecular details more accurately, a key question is: What is the shape of a molecule? In contrast to macroscopic objects, this cannot be determined with geometrical optics. An operational procedure to determine the shape is via repulsive forces that prevent interpenetration of bodies. However, also in contrast to the macroscopic domain, no fine tip is available for “scanning” the molecule. Instead, only similarly sized objects, namely other atoms or molecules (which carry own force fields) can be used. The resulting shape therefore depends on the ‘probe’. Nevertheless, using this kind of procedure, one gets exactly the information that is required when mutual accessibility of molecules is analyzed.
Two recent developments will be sketched that are based on this operational definition of molecular shape: (a) data-driven determination of effective atomic radii and (b) computation of molecular surfaces that characterize approachability by other molecules. The methods will be illustrated using analysis examples from biophysics, structural biology and drug design.

沈汉威

沈汉威 Ohio State University
Han-Wei Shen is a full professor at The Ohio State University. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View California. His primary research interests are scientific visualization and computer graphics. Professor Shen is a winner of National Science Foundation's CAREER award and US Department of Energy's Early Career Principal Investigator Award. He also won the Outstanding Teaching award twice in the Department of Computer Science and Engineering at the Ohio State University.

Keynote: Large-Scale Distribution-Based Data Analysis and Visualization

Abstract: To effectively analyze very large scale data sets, one often needs to first overview and identify regions of interest by transforming the data into compact information descriptors that allow detailed analysis on demand. Among many existing feature descriptors, statistical information derived from data samples is a promising approach to taming the big data avalanche because data distributions computed from a population can compactly describe the presence and characteristics of salient data features with minimal data movement. The ability to computationally summarize and process data using distributions also provides an efficient and representative capture of information that can adjust to size and resource constraints, with the added benefit that uncertainty associated with results can be quantified and communicated. In this talk, I will discuss applying distribution-based approaches for visualization and data analytics. I will first motivate our approach by presenting some examples of how statistical information can help in data visualization, and then present some of our research on multivariate analysis, vector/scalar field analysis, and histogram compression and query.

屈华民

屈华民 香港科技大学计算机与工程系
屈华民于西安交通大学取得数学本科学位,于纽约州立大学石溪分校取得计算机硕士和博士学位。主要从事可视化和计算机图形学研究。其研究领域涵盖科学可视化,信息可视化,和可视分析。已经发表超过70篇以上的研究论文,其中有20篇以上论文发表在可视化领域的顶级期刊IEEE Transactions on Visualization and Computer Graphics (TVCG)。 他是TVCG的编委(associate editor), IEEE Pacific Visualization系列会议的筹划指导委员会(Steering Committee)成员, PacificVis 2011和2012的程序委员会共同主席,和IEEE VIS’14的论文共同主席(paper co-chairs)。 担任过期刊 IEEE Computer Graphics and Applications和ACM Transactions on Intelligent Systems等的客座编辑。其研究获得过2009年IEEE可视化会议最佳论文提名奖,2009年IBM Faculty Award,和第十三届“计算机辅助设计与图形学国际会议”(CAD/Graphics 2013)最佳论文奖等。


Keynote: VisMOOC: Visualizing Video Clickstream Data from Massive Open Online Courses

Abstract: Massive Open Online Courses (MOOCs) are becoming increasingly popular and have attracted much research attention. Recent research shows that e-learners spend the majority of their time watching lecture videos. With thousands of students watching course videos, enormous amounts of clickstream data are produced and recorded for a single course. Such large-scale clickstream data for MOOC videos pose a special analytical challenge but provide a good opportunity for understanding how students interact with the videos, which in turn can help instructors and educational analysts gain insight into online learning behaviors. We have worked closely with the instructors of two Coursera courses to develop a visual analytics system to help them explore the clickstream data generated for their courses. With their assistance, we first came up with task specifications for such a system. After that, we propose a set of design goals for our system, discuss the design alternatives we considered, and justify the design choices we have made. A visual analytic system, VisMOOC, is thus developed iteratively through a complete user-centered design process. In this talk, I will first introduce the VisMOOC system. Then I will present several case studies made by the instructors and also the new findings obtained on learning behaviors for MOOCs.

陈宝权

陈宝权 山东大学
陈宝权,山东大学计算科学与技术学院与软件学院院长、教授,兼中国科学院深圳先进技术研究院研究员,博士生导师。电子工程学士(91年西安电子科技大学),硕士( 94 年清华大 学),计算机博士( 99 年纽约州立大学石溪分校)。研究方向为大规模城市场景三维获取及海量数据可视化,在 ACM SIGGRAPH 、 IEEE VIS、ACM TOG等国际会议和刊物发表论文 100 余篇。现任IEEE​ Transactions on Visualization and Computer Graphics编委;​任SIGGRAPH ASIA 2014会议主席、SIGGRAPH​ ASIA指导委员会委员、和IEEE ​ VIS指导委员会委员​;曾任IEEE 可视化会议 2005 年主席和 2004 年程序委员会主席。获 2003 美国 NSF CAREER Award, 2005 年 IEEE可视化国际会议最佳论文奖;2008 年入选中科院 " 百人计划 " ; 2010 年获国家杰出青年科学基金资助;2013年人选国家“百千万人才”工程计划和“中青年领军人才”。

Talk: 城市大数据可视分析Courses

Abstract: 基于城市场景产生的各类数据越来越多,构成了一个典型的大数据。数据的时空融合是城市大数据有效处理分析的基础,而城市场景的三维几何是数据的载体。本报告将介绍城市的三维场景获取与理解,数据的融合与分析,以及部分应用案例。

Koji Koyamada

Koji Koyamada Kyoto University
Koji Koyamada received a B.S., M.S. and Ph.D degrees in electronic engineering from Kyoto University, Kyoto, Japan in 1983, 1985, and 1994, respectively. He is a professor at Kyoto University. From 1985 to 1998 he worked for IBM Japan. From 1998 to 2001 he was an associate professor at Iwate Prefectual University. From 2001 to 2003 he was an associate professor at Kyoto University. His research interest includes modeling, simulation and visualization. He is a member of IEEE Computer Society, directors of Visualization Society Japan, and the Institute of Systems, Control and Information Engineers and a 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.

Talk: Visualization in the Scientific Method

Abstract: In this talk, we would like to consider a visualization category in a scientific research. The recent emphasis on visualization started in 1987 with the publication of Visualization in Scientific Computing (ViSC), a special issue of Computer Graphics. From then, three major categories, scientific visualization, information visualization and graph drawing, are formed in the visualization society but are not fit to the actual application. The scientific research is based on the scientific method in which we identify a problem in a society, form & test a hypothesis, and apply the tested hypothesis to the society. Several application examples show that in each phase of the scientific method, visualization techniques are employed regardless of the categories and we can confirm that these categories may be unified into scientific visualization if the application is based on the scientific method.

陈为

陈为 浙江大学
1976 年生,浙江大学计算机学院CAD&CG 国家重点实验室教授,博士生导师。1996 年本科毕业于浙江大学应用数学系,2000 年6月至2002 年6 月在德国Fraunhofer 图形研究所攻读联合培 养博士,2002 年9 月进入浙江大学工作,2009 年12 月晋升教授。2006 年7 月至2008 年9 月在美国普度大学从事访问研究。 研究领域是可视化与可视分析。发表国际一流学术期刊论文近30 篇,其中包括8篇可视化领域顶级期刊IEEE Transactions on Visualization and Computer Graphics 论文。在可视化顶级会议IEEE Visualization 上发表6 篇长文。获2009 年IEEE Visualization 最佳论文提名奖、2011 年Pacific Visualization 最佳Poster 奖。担 任国际著名学术会议程序委员会委员或分论坛主席多次(IEEE Visualization, EuroVis,IEEE Pacific Graphics, CGI, IEEE Pacific Vis, VINCI 等)。出版教材 一部,讲授课程被评为国家精品课程。2011 年获浙江省科学技术奖二等奖。

Talk: 城市人群移动可视分析

Abstract: 城市数据除了静态场景,还包括动态的目标,其中人群活动可以如实反映城市的经济、市政和管理等各个方面。本次报告将介绍城市人群数据的基本概念、数据和需求,并讨论潜在的可视分析问题。

冯文刚

冯文刚 中国公安大学反恐学院
博士,中国人民公安大学公安情报学系(现更名为反恐怖学院)讲师。从事公安情报相关领域的基础理论研究工作,为中国人民公安大学“情报研究中心”创新团队的主要成员之一,主持公安部软科学项目1项、中央高校基本科研业务费项目3项,并参与国家863项目、国际合作项目等多个项目,获国家发明专利授权1项,在国内外期刊和国际会议上发表论文十余篇。




Talk: 公安工作中可视化技术的需求

Abstract: The widespread use of Internet has led to information explosion, a daunting challenge we must address in order to make sense and maximize utilization of the available information. Visualization proves effective in uncovering the hidden structures and patterns in massive, dynamically changing information spaces. I will present techniques that my group at UC Davis has developed for visualizing and reasoning the behaviors of individuals or groups, the connections between them, and their evolutions.

张加万

张加万 天津大学
张加万,山东蒙阴人,博士、教授,软件工程专业的博士生指导教师,软件学院副院长。张教授是天津大学图形图像与可视计算实验室负责人、中国文化遗产保护与传承信息技术研究中心主任。他也是中国计算机学会高级会员、中国图象图形学学会理事、天津市图象图形学会副理事长、ACM Member、IEEE Member。天津市图象图形学会副理事长,中国图象图形学学会理事,中国计算机学会高级会员、学术工作委员会委员。其主要研究方向是可视计算、网络安全可视分析、文化遗产数字化保护。现承担国家科技支撑计划项目2项,以首席科学家承担国家社科基金重大项目1项。在国内外学术期刊ACM TOG、IEEE TVCG、IEEE TMM及国际会议包括ACM Siggraph、IEEE VAST、CVPR等发表学术论文50余篇。申请国家发明专利20余项,已获得授权12项。获得天津市科技发明奖二等奖1项,三等奖20余项,科技进步三等奖1项。2005、2006、2012年获全球IBM Faculty Award。2011年获Google奖教金。2013年入选教育部“新世纪优秀人才支持计划”。2013年国家社科基金重大项目首席科学家。2014年天津市青年科技奖获得者。

Talk: 网络安全可视分析

Abstract: 网络安全面临越来越多的挑战,尤其是对复杂场景例如网络安全状态感知、安全风险的识别与应对、敏捷的安全决策支持等都需要可视化、人机交互为中心的网络数据可视分析技术。本报告将从网络安全分析任务、网络安全数据、可视分析技术等角度出发,对网络安全可视化和可视分析的发展进行简短回顾、综述已经出现的主要网络安全可视化和可视分析技术与系统,并对网络安全可视分析领域面临的挑战和机会进行展望。

陶煜波

陶煜波 浙江大学
陶煜波,博士,浙江大学CAD&CG国家重点实验室副教授。于2003年、2009年分别获得浙江大学学士、博士学位。之后在浙江大学进行博士后研究,2010年至2012年在英国贝德福德大学作为博士后从事生物医学可视化研究。 2012年9月进入浙江大学CAD&CG国家重点实验室工作,已主持国家自然科学基金青年基金项目1项。研究兴趣包括电磁计算、科学计算可视化和可视分析。在国际一流学术期刊和会议发表论文多篇,包括IEEE TVCG,IEEE TIM,IEEE TAP等。多次受邀参加国际学术期刊和会议的评审工作。

Talk: 体数据的上采样与去噪

Abstract: 为了有效地分析和可视化体数据,需要对低分辨率体数据和数据中的噪声进行预处理。报告将介绍基于自身相似性的体数据上采样方法和梯度域上的体数据去噪算法。

周霞

周霞 中国石化胜利油田物探研究院
周霞,女,长期从事油田企业勘探数据库建设、应用软件开发及项目管理等工作。先后主持、参与中石化及胜利油田科技项目十余项,并成功应用及推广到中石化各油田。现为中国石化胜利油田物探研究院数据库与信息技术首席专家、中国计算机学会CCF会员、大数据专委会委员、“产学研用”工作组委员。中国石化信息技术项目评审专家库成员。

Talk: 石油勘探可视分析技术应用

Abstract: 从油气勘探综合研究工作的业务流程入手论述石油勘探与可视分析的关系,并介绍了胜利油田在油气勘探数据可视化应用方面的技术与成果。

梁荣华

梁荣华 浙江工业大学
梁荣华,博士、教授、博导,浙江工业大学信息工程学院副院长。主要研究方向为图形图像处理、信息可视化及可视分析。他于2003年获浙江大学计算机专业博士学位,2004.4-2005.6在英国Bedfordshire大学做Research Fellow, 2010.3-2011.3在美国UCDAVIS做访问学者。 他获得浙江省科学技术二等奖2项(1项排名第一,1项排名第三)、第十四届中国专利优秀奖1项(排名第一),获得授权发明专利10项,在顶级期刊和会议如IEEE TKDE、IEEE TVCG和IEEE VIS等发表论文70多篇。梁荣华教授主持了科技部中小企业中欧国际合作、国家自然科学基金、浙江省重大科技专项、浙江省自然科学基金重点项目等10多个项目的研究。梁荣华教授于2007年获浙江省中青年学科带头人、浙江省优秀博士后,2012年入选教育部新世纪优秀人才支持计划,2014年获得浙江省杰出青年科学基金。

Talk: 交通大数据的处理和可视分析

Abstract: 我们面临大数据时代,如何以图形方式直观展示和交互分析这些数据的规律,幷指导我们做预测和决策,是目前的研究热点。本报告将讲述出租车定位大数据的处理和可视分析方法,我们将重点讲述如何处理数据,如何对数据进行可视化编码,及如何提供交互分析方法等。通过这些方法,我们能了解数据背后的规律。

王立勋

王立勋 北京市交通信息中心
王立勋,男,1981年3月出生,北京市交通信息中心规划发展部副部长,工程师。主要主持智能交通的前沿理论跟踪和应用技术研发工作。作为项目负责人参与2010、2013年“核高基”国家科技重大专项,参与极端天气条件下保持道路交通畅通物联网应用示范工程、基于车联网技术的公交车载智能终端与应用示范、北京市城市公共交通智能化应用示范工程等多项地方和省部级科研项目、示范工程建设。取得国家计算机软件著作权3项,专利1项,参与1项地方标准修订,3项行业标准编写,发表论文10余篇。

Talk: 大数据在交通应急

Abstract:

陈莉

陈莉 清华大学
陈莉于1997年在浙江大学获得博士学位并留在CAD&CG国家重点实验室工作, 2000年--2006年先后在日本高度情报科学技术研究机构及东京大学作研究员,从事大规模数据实时并行可视化方面的研究和软件开发,作为骨干参与了三个日本文部省重大资助研究项目,研发的并行可视化软件在日本的超级计算机上使用。2007年至今在清华大学软件学院计算机图形与辅助设计研究所工作,从事数据可视化、并行算法、CAE软件研发工作,作为负责人承担过国家自然科学基金重点项目、面上项目、中日重大国际合作项目等,作为子课题负责人承担了十一五、十二五863重点项目、国家重大专项、中美重大国际合作项目等,在期刊和会议上发表论文七十多篇。

Talk: 大规模城市地震灾变与疏散仿真数据的可视化与可视分析

Abstract: 将围绕大规模城市地震灾变与疏散仿真数据的可视化中的一些关键问题以及我们目前采用的方法进行介绍,包括城市建筑群的多尺度仿真模型自动构建;地震波扩散数据、GIS数据、建筑物数据、建筑物灾变响应数据的实时混合可视化;对Multi-agent疏散仿真结果数据的可视分析,从而帮助研究者更好地制定城市灾变应对方案。

苏卫

苏卫 胜利油田物探研究院勘探数据库室
苏卫,男,工学硕士,2006年毕业于中国石油大学(华东)计算机与通信学院。目前在中国石化胜利油田分公司物探研究院勘探数据库室工作,主要从事勘探数据集成分析、计算机辅助决策、三维可视化与虚拟现实研究等工作。

Talk: 三维可视化在石油勘探中的应用

Abstract: 选取石油勘探中井筒数据、地震数据等典型的几类勘探数据,介绍了其数据特点、相应的建模方案以及数据的三维可视化的实现过程。

乐阳 深圳大学
乐阳,副教授。深圳大学土木工程学院交通工程系教师,原武汉大学地图学与地理信息专业博士生导师。香港大学城市规划及环境管理中心博士,研究方向包括移动对象时空模型、表达及管理,基于城市和交通的群体移动对象管理与分析,以及个体移动对象行为规律分析与挖掘等。

Talk: 区域划分及尺度不确定性问题对地理分析的影响

Abstract: 动态数据在空间分析中存在不确定性问题。以手机定位数据识别城市中心为例,说明可塑性面积单元问题和不确定的地理情境问题。群体活动强度的空间自相关程度受到采样区域划分方式和分析单元大小的影响,地理情境的时空动态变化也会带来不确定的地理情境问题。可塑性面积单元问题和不确定的地理情境问题是不可忽视的问题。


VIS论文报告讲者

巫英才

巫英才 微软亚洲研究院
巫英才,微软亚洲研究院研究员。他于2009年在香港科技大学获得计算机科学博士学位,2010到2012年在加州大学戴维斯分校从事博士后研究工作。迄今为止已在国际会议和期刊发表学术论文30余篇,其中包括14篇可视化顶级期刊(IEEE TVCG),在可视化顶级国际会议IEEE VIS上发表11篇长文(三篇IEEE InfoVis, 四篇IEEE SciVis,四篇IEEE VAST),其中一篇论文获得IEEE SciVis 2009年会最佳论文提名奖。他是多个可视化国际会议的程序委员会委员(IEEE SciVis, PacificVis, VINCI, VDA),担任VINCI 2014的程序委员会主席(Paper Co-chairs), IEEE PacificVIs 2014短论文联合主席(Notes Co-chairs),以及国际期刊IJSEKE的客座编委。

Talk: OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media

Abstract: Analyzing and tracing the diffusion of public opinions on social media can find many different applications such as government and business intelligence. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we develop a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to interactively select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.

鄂艳丽

鄂艳丽 天津大学
鄂艳丽,天津大学硕士研究生在读,IEEE Student member,2012年获得南京理工大学学士学位。研究生期间参加了十二五国家科技支撑计划重点项目一项,发表领域内顶级会议IEEE VIS论文一篇(收录于TVCG, CCF计算机图形学与多媒体A类,SCI检索)

Talk: Visual Analysis of Public Utility Service Problems in a Metropolis

Abstract: Issues that citizens reported about utility service can provide unprecedented insights into the various aspects of utility service. Analysis of the issues can improve living quality through evidence-based decision making. However, these issues are complex, containing spatial and temporal components in addition to multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. In this paper, we propose a visual analytics process based on the main tasks of city utility service management to analyze these issues. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations to represent events. In addition, we provide a set of tools and interaction techniques to explore issues. Our approach enables administrators to make more informed decisions

陈海东

陈海东 浙江大学
陈海东是浙江大学计算机科学与技术学院博士研究生。主要研究领域为不确定性可视化,可视分析。

Talk: Visual Abstraction and Exploration of Multi-class Scatterplots

Abstract: Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.

孙国道

孙国道 浙江工业大学
孙国道是浙江工业大学信息工程学院博士研究生,2010年在浙江工业大学计算机科学技术学院获得学士学位。主要研究方向为社交媒体数据可视化,交通数据可视化。其在可视化系列会议IEEE Visualization, IEEE Pacific Visualization均发表过文章(含已录用文章)。

Talk: EvoRiver: Visual Analysis of Topic Coopetition on Social Media

Abstract: Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the cooperation process by depicting how different groups of influential users (i.e., "topic leaders") affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).

王希廷

王希廷 MSRA
Xiting Wang is a PhD candidate in the Institute for Advanced Study at Tsinghua University, China. She received a BS degree in Electronics Engineering from Tsinghua University. Her research interests include visual text analytics and text mining.

Talk: TopicPanorama: a Full Picture of Relevant Topics

Abstract: We present a visual analytics approach to developing a full picture of relevant topics discussed in multiple sources such as news, blogs, or micro-blogs. The full picture consists of a number of common topics among multiple sources as well as distinctive topics. The key idea behind our approach is to jointly match the topics extracted from each source together in order to interactively and effectively analyze common and distinctive topics. We start by modeling each textual corpus as a topic graph. These graphs are then matched together with a consistent graph matching method. Next, we develop an LOD-based visualization for better understanding and analysis of the matched graph. The major feature of this visualization is that it combines a radially stacked tree visualization with a density-based graph visualization to facilitate the examination of the matched topic graph from multiple perspectives. To compensate for the deficiency of the graph matching algorithm and meet different users' needs, we allow users to interactively modify the graph matching result. We have applied our approach to various data including news, tweets, and blog data. Qualitative evaluation and a real-world case study with domain experts demonstrate the promise of our approach, especially in support of analyzing a topic-graph-based full picture at different levels of detail.

吴文超

吴文超 香港科技大学
吴文超,博士研究生。于上海交通大学信息安全与工程学院取得学士学位,并分别于美国佐治亚理工学院电子与计算机工程系(Electrical and Computer Engineering)和上海交通大学信息安全与工程学院取得双硕士学位。目前就读于香港科技大学计算机与工程系攻读博士学位,并获得香港政府“Hong Kong PhD Fellowship Scheme”资助。主要从事数据可视化分析、信息可视化和数据挖掘等研究。

Talk: BoundarySeer: Visual Analysis of 2D Boundary Changes

Abstract: Boundary changes exist ubiquitously in our daily life. From the Antarctic ozone hole to the land desertification, and from the territory of a country to the area within one-hour reach from a downtown location, boundaries change over time. With a large number of time-varying boundaries recorded, people often need to analyze the changes, detect their similarities or differences, and find out spatial and temporal patterns of the evolution for various applications. In this paper, we present a comprehensive visual analytical system, BoundarySeer, to help users gain insight into the changes of boundaries. Our system consists of four major viewers: 1) a global viewer to show boundary groups based on their similarity and the distribution of boundary attributes such as smoothness and perimeter; 2) a region viewer to display the regions encircled by the boundaries and how they are affected by boundary changes; 3) a trend viewer to reveal the temporal patterns in the boundary evolution and potential spatio-temporal correlations; 4) a directional change viewer to encode movements of boundary segments in different directions. Quantitative analysis of boundaries (e.g. similarity measurement and adaptive clustering) and intuitive visualizations (e.g. density map and ThemeRiver) are integrated into these viewers, which enable users to explore boundary changes from different aspects and at different scales. Case studies with two real-world datasets have been carried out to demonstrate the effectiveness of our system.

任东昊

任东昊 University of California, Santa Barbara,北京大学
任东昊毕业于北京大学,现在是 University of California, Santa Barbara 的博士研究生。主要研究领域为信息可视化。

Talk: iVisDesigner: Expressive Interactive Design of Information Visualizations

Abstract: We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, discuss its limitations and exemplify it through a variety of illustrative visualization designs. A performance analysis and an informal user study are presented to evaluate the system.

郭翰琦

郭翰琦 北京大学
郭翰琦于2014年在北京大学信息科学与技术学院获得博士学位,2009年在北京邮电大学理学院获得学士学位。2014年秋将赴美国阿贡国家实验室开展博士后研究工作。主要研究方向为大规模数据可视化,体数据可视化, 以及多变量数据可视化等。发表十余篇国际可视化方向高水平会议、期刊论文,包括IEEE TVCG、IEEE Visualization (SciVis)、IEEE Pacific Visualizaiton等。

Talk: Advection-Based Sparse Data Management for Visualizing Unsteady Flow

Abstract: When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.

洪帆

洪帆 北京大学
洪帆是北京大学信息科学技术学院博士研究生。2012年在南京大学计算机科学与技术系获得学士学位。主要研究领域为流场可视化,多变量科学数据可视化。

Talk: FLDA: LDA Based Unsteady Flow Analysis

Abstract: In this paper, we present a novel feature extraction approach called FLDA for unsteady flow fields based on Latent Dirichlet allocation (LDA) model. Analogous to topic modeling in text analysis, in our approach, pathlines and features in a given flow field are defined as documents and words respectively. Flow topics are then extracted based on Latent Dirichlet allocation. Different from other feature extraction methods, our approach clusters pathlines with probabilistic assignment , and aggregates features to meaningful topics at the same time. We build a prototype system to support exploration of unsteady flow field with our proposed LDA-based method. Interactive techniques are also developed to explore the extracted topics and to gain insight from the data. We conduct case studies to demonstrate the effectiveness of our proposed approach.

王祖超

王祖超 北京大学
王祖超是北京大学信息科学技术学院博士研究生。主要研究领域为交通可视化,轨迹数据可视化。

Talk: Visual Exploration of Sparse Traffic Trajectory Data

Abstract: In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macrotraffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.

石丛磊)

石丛磊 HKUST
Conglei Shi is currently a PhD candidate in the Department of Computer Science and Engineering at HKUST(Hong Kong University of Science and Technoledge). His current supervisor is Prof. Huamin Qu. His research interest includes information visualization, visual analytics, and human computer interaction. Before coming to HKUST, he has got my B.Sc. Degree study in SJTU(Shanghai Jiao Tong University) in major of Computer Science. During his undergraduate studies, he was a member of the BCMI Lab and supervised by Prof. Baoliang Lu.

Talk: LoyalTracker: Visualizing Loyalty Dynamics in Search Engines

Abstract: The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines.