屈华民, 香港科技大学
    授课题目: 可视化+：可视化的跨界和融合
    时间: 7 月 17 日 09:00 - 12:00

课程摘要 : 首先我会简要回顾一下可视化的历史，不同的子学科，以及可视化在大数据和人工智能时代所能扮演的角色。 然后我会介绍可视化和相关学科的关系，可视化的上游（+可视化）和下游 （可视化+），侧重探讨可视化和别的学科的交叉和融合，以及评价可视化的标准。 最后我会介绍可视化在智慧城市，在线教育，社交媒体，以及可解释性人工智能等领域的应用。

讲者简介 : 屈华民现任香港科技大学计算机与工程系教授、 可视化和人机交互实验室主任。本科毕业于西安交通大学数学系，2004 年于纽约州立大学石溪分校 取得计算机博士学位。屈教授的研究领域是数据可视化和人机交互，他发表超过 100 篇学术论文， 其中 超过40篇发表于可视化领域的顶级期刊《IEEE Transactions on Visualization and Computer Graphics (TVCG)》。他的研究得过多项奖励，包括 8 项最佳论文奖 /荣誉提名奖， 2009 IBM 教师奖，2015 年 度香港资讯及通讯科技奖 - 最佳创新奖 (创新科技) 银 奖，以及2015 华为诺亚方舟实验室杰出合作者奖。他是 IEEE VIS’14 （SciVis)， VIS’15 (SciVis), 以及 VIS’18 （VAST） 的 论 文 联 合 主 席 、 IEEE PacificVis'11 和 IEEE PacificVis'12 程序联合主席。

    Leila De Floriani, 马里兰大学帕克分校
    授课题目 : Topology-Based Methods for Spatial Data Analysis and Visualization
    时间: 7 月 17 日 14:00 - 17:00

课程摘要 : Analyzing large spatial data sets requires efficient data management techniques, powerful analysis algorithms and visualization methods which allow domain experts to effectively interact with data. Advanced tools from combinatorial topology, such as persistent homology and Morse theory, provide a theoretically well-justified, and parameter-free way to extract the complex intrinsic structures of data in a very concise format, but are computationally intensive for current large-size data sets. In this talk I will present our work in topological analysis of big spatial data, specifically point data equipped with one or more function values: scalar fields (terrains, 2D or 3D images, unstructured volume data sets, etc.), and multifields, which are collections of fields with different modalities (e.g., pressure and density in physical simulations). will focus on topology-based visual analytics tools to support interactive data analysis, and discuss scalability issues.

讲者简介 : Leila De Floriani is a Professor at the University of Maryland at College Park. She has been Professor of Computer Science at the University of Genova in Italy since 1990, where she founded the Geometry and Computer Graphics group, and where she has been the Director of the PhD Program in Computer Science for eight years. She has also held positions at the Italian National Research Council, at the University of Nebraska, at Rensselaer Polytechnic Institute, and at the University of Maryland. Leila De Floriani has written over 300 publications in the fields of geometric and solid modeling, shape analysis, scientific data visualization, terrain modeling and geospatial data processing, which have appeared in major international journals and conferences. The main focus of her current research is in topological data analysis, geometric and topological data representations, geometric algorithms for scientific visualization and topology-based visual analytics. Leila De Floriani is the Editor-in-Chief of the IEEE Transactions on Visualization and Computer Graphics. She is currently an Associate Editor of Graphical Models and of the ACM Transactions on Spatial Algorithms and Systems, and of GeoInformatica. She served on more then 150 program committees of the major international conferences in geometric modeling, computer graphics, visualization, and geospatial data processing. She is a Fellow of the IEEE, a Fellow of the International Association for Pattern Recognition (IAPR), a Member of the ACM and of the Eurographics Association. She is serving as a Member of the IEEE Computer Society Board of Governors for the years 2017-2019.

    Hanan Samet, 马里兰大学帕克分校
    授课题目 : Reading News with Maps by Exploiting Spatial Synonyms
    时间: 7 月 17 日 14:00 - 17:00

课程摘要 : NewsStand is an example application of a general framework to enable people to search for information using a map query interface, where the information results from monitoring the output of over 10,000 RSS news sources and is available for retrieval within minutes of publication. The advantage of doing so is that a map, coupled with an ability to vary the zoom level at which it is viewed, provides an inherent granularity to the search process that facilitates an approximate search thereby permitting the use of spatial synonyms instead of being limited to an exact match of a query string. This is predicated on the use of a textual specification of locations rather than a geometric one, which means that one must deal with the potential for ambiguity. The issues that arise in the design of a system like NewsStand, including the identification of words that correspond to geographic locations, are discussed, and examples are provided of its utility. More details can be found in the video at http://vimeo.com/106352925 which accompanies the ``cover article'' of the October 2014 issue of the Communications of the ACM about NewsStand at http://tinyurl.com/newsstand-cacm or a cached version at at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf.

讲者简介 : Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park. He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof-carrying code. He is the author of the recent book "Foundations of Multidimensional and Metric Data Structures" (http://www.cs.umd.edu/~hjs/multidimensional-book-flyer.pdf) published by Morgan-Kaufmann, an imprint of Elsevier, in 2006, an award winner in the 2006 best book in Computer and Information Science competition of the Professional and Scholarly Publishers (PSP) Group of the American Publishers Association (AAP), and of the first two books on spatial data structures "Design and Analysis of Spatial Data Structures", and "Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS", both published by Addison-Wesley in 1990. He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and Systems (TSAS), founding chair of ACM SIGSPATIAL, and a Fellow of ACM, IEEE, AAAS, IAPR (International Association of Pattern Recognition), and UCGIS (University Consortium for Geographic Science). He is a recipient of a Science Foundation of Ireland (SFI) Walton Visitor Award at the Centre for Geocomputation at the National University of Ireland at Maynooth (NUIM), 2009 UCGIS Research Award, 2011 ACM Paris Kanellakis Theory and Practice Award, and 2014 IEEE Computer Society Wallace McDowell Award. He has had a number of best paper awards including at the 2008 SIGMOD and SIGSPATIAL conferences. He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and was an ACM Distinguished Speaker.

    Peter Eades, 悉尼大学
    授课题目 : Graph Drawing
    时间: 7 月 18 日 09:00 - 12:00

课程摘要 : 1. How to draw a small graph:
--- Readability;  planar graphs;  straight-line drawings;  orthogonal drawings
2. How to draw a large graph
--- Faithfulness;   simple force-directed methods;   large-scale force-directed methods;   spectral methods

讲者简介 : Professor Peter Eades has been investigating methods for visualization of networks since the 1980s. The algorithms described in his papers on this topic are currently commonly used in diverse software systems, from social networks, biological networks, CASE tools, to security. Peter is currently semi-retired, however he continues to research the mathematics and algorithmics of geometric graphs.

    张小龙, 宾夕法尼亚州立大学
    授课题目 : 用基于地图的可视化方法支持意义构建
    时间: 7 月 18 日 14:00 - 17:00

课程摘要 : 本课程将侧重于如何利用基于地图的可视化方法来支持可视分析中的一个重要行为：意义构建。首先将介绍基于地图的可视化的基本概念、基于百度地图的可视化方法和手段、基于地图的可视化和可视分析所面临的一些挑战。然后通过两个研究课题来讨论基于地图的可视化方法在支持意义构建方面的作用。

讲者简介 : 张小龙博士是美国宾夕法尼亚州州立大学信息科学与技术学院副教授，该学院知识可视化实验室主任。其主要研究涉及人机交互、虚拟现实、信息可视化与可视分析、社交网络分析、协同系统等领域，目前在研课题包括大数据可视分析、社交网络分析、可视分析的方法与理论等。所承担课题由美国国家科学基金委等机构资助。相关的研究论文发表于International Journal of Human Computer Studies, Journal of Visual Languages and Computing, IEEE Transactions on Visualization and Computer Graphics等国际期刊，以及ACM CHI, IEEE VIS等国际会议。张博士是中国计算机学会人机交互专委会委员、中国图象图形学会可视化与可视分析专委会委员。曾担任宾州州立大学数字科学委员会交互技术小组组长、宾州州立大学信息科学与技术学院人机交互领域负责人、以及美国信息科学与技术协会亚利桑那州分会会长等职。张博士获清华大学学士和硕士学位、密歇根大学博士学位。曾执教于亚历桑那大学。

    赵盛东, 新加坡国立大学
    授课题目 : The Next Interaction Paradigm
    时间: 7 月 19 日 09:00 - 12:00

课程摘要 : We live in an interesting time. Although we don’t know exactly what the future will be like, transformational change is a certainty. There are several noticeable trends: one is the change of the computing paradigm from reactive to pro-active. All major companies are working on this paradigm shift, investing heavily in AI and machine learning. This will redefine the role and responsibility of our personal devices and dramatically change our lifestyles over the next few years. Another interesting trend is the interaction shift from device-centric to human-and-environment-centric, which has the potential to allow humans to return to a more natural way of living. Inside this movement, a central enabling piece is AR technology, but this is not without significant challenges. It will be exciting to work on the problems to accelerate this paradigm transformation, to which I hope the Chinese research community can make a significant contribution.

讲者简介 : Dr. Shengdong Zhao is an Associate Professor in the Department of Computer Science, National University of Singapore. He established the NUS-HCI research lab. Dr. Zhao completes his Ph.D. degree in Computer Science at the University of Toronto. He also holds a Master’s degree in Information Management & Systems from the University of California at Berkeley. Dr. Zhao has a wealth of experience in developing new interface tools and applications (i.e., Draco, won best iPad App of the year in 2016), and publishes regularly in top HCI conferences and journals. He also works closely with the industry, and currently serves as a senior consultant with the Huawei Consumer Business Group. Dr. Zhao frequently serves in program committees of top HCI conferences, and will work as the paper chair for the ACM SIGCHI 2019, and 2020 conferences. More information about Dr. Zhao and the NUS-HCI labcan be found at http://www.shengdongzhao.com and http://www.nus-hci.org.

    胡一凡, 雅虎研究院
    授课题目: Graph and Map Visualization with Applications to Machine Learning
    时间: 7 月 19 日 14:00 - 17:00

课程摘要 : This lecture is divided into two parts. In the first part, we look at the basic algorithms for visualizing graphs and high dimensional data, as well as the state of the art for large graph visualization. We also discuss algorithms for visualizing clusters in a point cloud as a virtual map, and the associated algorithmic problems. In the second part, we argue that there is actually a close connection between visualization and machine learning. We look at how to use visualization technique, including graph and map visualization, to help explain machine learning techniques such as recommender systems and gender/ethnicity classifiers. Finally we look at how to use a successful technique from visualization to improve deepwalk, a machine learning algorithm.
    Outline of the lecture :
  - graph visualization: basic concepts
  - dimensional reduction (MDS, PCA, t-SNE, word2vec)
  - state of the art in large graph visualization
  - virtual map visualization
  - application of visualization to machine learning
  - Using visualization technique to improve ML

讲者简介 : Yifan Hu is a Senior Director of Research at Yahoo! Research. Prior to joining Yahoo!, he worked at AT&T Labs, Wolfram Research, and at Daresbury Laboratory. He received his B.S. and M.S. in applied mathematics from Shanghai Jiao-Tong University, and Ph.D. in optimization from Loughborough University. His research interests include data mining, applied machine learning and visualization. He is a co-author of a number of best papers, including the 2017 ICDM 10-year highest impact award paper about recommender systems. He also contributes to the open source software Graphviz.

    沈汉威, 俄亥俄州立大学
    授课题目 : In-Situ Data Modeling Analysis and Visualization
    时间: 7 月 20 日 09:00 - 12:00

课程摘要 : Scientists overview and identify regions of interest by transforming data into compact information descriptors that characterize simulation results and 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 in situ 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 the results can be quantified and communicated. In this tutorial, I will discuss several recent works on using distributions as a new paradiagm for in situ data representation of large scale scientific data sets. The goal is to ensure that scientists can easily obtain an overview of the entire data set regardless of the size of the simulation output; understand the characteristics and locations of features; easily interact with the data and select regions and features of interest; and perform all the analysis tasks with a small memory footprint.

讲者简介 : 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 has served as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics, a paper chair for IEEE Visualization, IEEE Pacific Visualization, and IEEE Parallel Visualization and Graphics. He is currently on the IEEE Visualization conference executive committee, and IEEE SciVis steering committee. He has published more than 40 papers in IEEE Transactions on Visualization and Computer Graphics and IEEE Visualization conference, the very top journal and conference. A detailed list of his publications can be found in DBLP: http://dblp.unitrier. de/pers/hd/s/Shen:Han=Wei and Google Scholar: https://scholar.google.com/citations?user=95Z6-isAAAAJ&hl=en

    巫英才, 浙江大学
    授课题目 : 社交媒体数据的可视分析
    时间: 7 月 20 日 14:00 - 17:00

课程摘要 : 随着社交媒体比如新浪微博的高速发展，社交媒体所产生的数据呈现爆炸性增长的趋势。对这种数据进行有效的分析具有非常广阔的商业价值和研究价值。但是，社交媒体数据的海量规模以及复杂性，使得对这种数据进行有效地、实时地分析和探索变得异常困难。交互的可视分析的技术，在近年来被越来越多地运用到社交媒体数据的分析上，并取得了一定的成效。本报告首先简要介绍社交媒体数据可视分析的基本方法、任务与应用，并结合我在相关领域的工作，介绍国内外的最新进展及其未来的展望。

讲者简介 : 巫英才是浙江大学CAD&CG国家重点实验室百人计划研究员、博士生导师，入选国家青年千人计划，担任浙江大学计算机科学与技术学院院长助理。他的主要研究方向是可视分析、信息可视化和人机交互。他2009年从香港科技大学获得计算机科学博士学位，2010年5月到2012年3月在加州大学戴维斯分校从事博士后研究工作，2012年5月至2015年1月在微软亚洲研究院任研究员。他迄今为止已经在国际会议和期刊发表学术论文60余篇。他是亚太可视化年会IEEE Pacific Visualization 2017、中国可视化年会ChinaVis 2016和2017等的论文主席，并担任中国图象图形学学会人机交互专委会副主任。详情请见www.ycwu.org

    时磊, 中国科学院软件研究所
    授课题目 : 可视分析在人类脑部网络比较与深度神经网络解释上的应用
    时间: 7 月 21 日 09:00 - 12:00

课程摘要 : 可视分析，作为计算机科学的前沿交叉学科，在数据到知识的过程中起到关键的桥梁作用并提供重要的工具支撑。本讲座将重点介绍可视分析方法在人类及人工神经网络分析方面的应用。首先，针对不同人群的脑部网络比较问题，我们研究了如何建立可视化与数据挖掘目标的联合模型，并提出了针对脑部网络比较的交互式可视分析框架BrainQuest。第二，针对利用财经新闻预测股票价格的深度神经网络模型，我们研究了如何从模型中抽取可解释的关键文本因素，并利用可视分析方法辅助用户理解模型的问题。通过优化深度神经网络模型以提升可解释性并引入改进的层次相关性传播算法，我们提出了一套股价预测模型可视解释系统DeepClue，并通过案例证明了其有效性。最后，我们总结了可视分析在上述应用中的优势与局限性，并给出了本领域未来工作的几点展望。

讲者简介 : 时磊，现任中科院软件所计算机科学国家重点实验室研究员。2003、2008年毕业于清华大学计算机系，获工学学士、博士学位，其博士学位论文获清华大学计算机系优秀博士论文。曾任清华大学计算机系研究生团总支书记、IBM中国研究院可视分析组研究经理。博士及工作期间曾获全额资助在美国圣母大学、纽约大学、亚利桑那州立大学、IBM华生研究院、AT&T香农实验室、Yahoo实验室、国立台湾清华大学、微软亚洲研究院等著名研究机构学术访问十余次。主要研究方向为可视分析、数据挖掘、计算机网络。曾在IEEE TVCG, TC, VIS, ICDE, Infocom, ACM Sigcomm, CSCW, PIEEE等国际顶尖会议及期刊上发表70余篇研究论文或图书章节，Google Scholar显示论文总引用1700余次。四次荣获IEEE可视分析大会挑战赛优胜奖及IBM研究机构可视分析贡献奖。现为IEEE高级会员，任TVCG、TOCHI、TPDS、JSAC等顶尖国际期刊，KDD、IJCAI、ICDM、VIS, EuroVis、PacificVis、GD等高水平国际会议程序委员会委员或审稿人，IEEE ICDM、ACM CIKM等大会附属研讨会论文主席，电子学报英文版、软件学报英文版可视分析专刊特邀编委。现主持或作为骨干参加国家自然科学基金、973等项目5项。2016年度入选中国科学院青年创新促进会，2017年度入选中国科学院软件研究所杰出青年人才发展专项计划。

    麻晓娟, 香港科技大学
    授课题目 : Exploring Interaction Space of Data Displays
    时间: 7 月 21 日 14:00 - 17:00

课程摘要 : This lecture provides an overview of different types of data displays, including data visualization, data physicalization, and data sonification. We further introduce the different levels and kinds of interaction space around each type of display that can be leverage to enrich users' experience with data.

讲者简介 : Xiaojuan Ma is an assistant professor of Human-Computer Interaction (HCI) at the Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST). She received the Ph.D. degree in Computer Science at Princeton University. She was a post-doctoral researcher at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University (CMU), and before that a research fellow in the National University of Singapore (NUS) in the Information Systems department. Before joining HKUST, she was a researcher of Human-Computer Interaction at Noah\'92s Ark Lab, Huawei Tech. Investment Co., Ltd. in Hong Kong. Her background is in Human-Computer Interaction. She is particularly interested in data-driven human-engaged computing in the domain of ubiquitous, social, and crowd computing and Human-Robot Interaction.

    Michael McGuffin, 加拿大ETS
    授课题目: Novel Interaction Techniques
    时间: 7 月 22 日 09:00 - 12:00

课程摘要 : 1) Generating and Testing Ideas with Sketching and Paper Prototyping
                2) Popup Widgets for Fast, Gestural Interaction

讲者简介 : Michael McGuffin is an Associate Professor in the Department of Software and IT Engineering at ETS in Montreal, Quebec, Canada. ETS is the "École de technologie supérieure", a French-language engineering school within a provincial network of institutions called the University of Quebec. Along with his master's and PhD students, Michael conducts research in information visualization and Human-Computer Interaction (HCI). He has published six papers cited more than 100 times each, and in 2009, his paper at the IEEE Information Visualization Conference (InfoVis 2009) received an Honorable Mention. He has also served during multiple years on the program committee for IEEE InfoVis. Previously, Michael was a post-doctoral researcher at the Ontario Cancer Institute, working on visualizations and user interfaces for bioinformatics, within Dr. Igor Jurisica's lab. He completed a Ph.D. in Computer Science at the University of Toronto, where his homebase was the Dynamic Graphics Project (DGP) lab, and where his advisor was Prof. Ravin Balakrishnan. He also holds an Honours Bachelor of Applied Science (B.A.Sc.) in Computer Engineering with Software Engineering Option from the University of Waterloo. Prior to his graduate studies, he worked as a software developer, creating user interfaces at Alias|wavefront in Toronto and Discreet Logic in Montreal (both companies now part of Autodesk), and CAE in Montreal. Michael hails from Chibougamau, Quebec, Canada, and enjoys living in a trilingual household.

    刘世霞, 清华大学
    授课题目 : 可解释机器学习：破解AI的思维“黑箱”
    时间: 7 月 22 日 14:00 - 17:00

课程摘要 : 可解释的机器学习旨在使机器学习模型的决策过程对研究人员和从业人员更加透明，从而实现人机的有效沟通和协作。本报告将介绍基于可视分析的可解释机器学习研究现状、主流方法及技术挑战。特别是如何将机器学习方法和交互可视化方法有机地结合在一起，从而更好地帮助用户理解复杂模型工作机理及其输出结果，分析、诊断并不断完善机器学习模型。

讲者简介 : 刘世霞博士是清华大学软件学院的长聘副教授。主要研究方向是可视分析、文本挖掘工作和信息可视化。担任 CCF A类会议 IEEE VIS(VAST) 2016和 2017的论文主席；担任 IEEE Transactions on Visualization and Computer Graphics 编委（ Associate editor）；担任 IEEE Transactions on Big Data 编委（ Associate editor）；担任国际可视化会议 IEEE Pacific Visualization 2015的程序委员会主席。同时她是 Information Visualization期刊的编委，也是多个国际会议的程序委员会委员，例如 InfoVis、VAST 、CHI、KDD、 ACM Multimedia、ACM IUI 、SDM和 PacificVis等。担任IEEE VIS 2014 Meetup 共同主席（ IEEE VIS组织委员会）和IEEE VIS 2015 Tutorial共同主席（ IEEE VIS组织委员会）。要了解更多信息，请访问她的个人主页：http://cgcad.thss.tsinghua.edu.cn/shixia/。