课程教师: 沈汉威（俄亥俄州立大学）
    题目: 数据分析与可视化信息理论(2018.7.25 14:00-17:00)
    摘要: 信息理论已被用于研究通过嘈杂的通信信道可靠传输消息的基本限制，最初由Claude Shannon和Norbert Wiener在20世纪40年代后期提出。到目前为止，信息理论已经在许多领域产生了深远的影响，不仅在传输领域，在物理学，心理学和艺术领域等也影响深远。本报告将从信息论角度出发，将视觉分析的过程视为一种视觉通信渠道，试图将数据中的信息传达给观众。首先介绍一些基本的信息理论指标，用于量化数据集中的信息。然后，介绍信息理论在科学和信息可视化中的各种应用。本课程面向普通读者，特别适用于正在寻找新的研究课题并希望拓宽研究视野的学生和研究人员。
    课程教师简介: 沈汉威是俄亥俄州立大学的全职教授。他于1988年获得台湾大学计算机科学与信息工程专业学士学位，1992年获得纽约州立大学石溪分校计算机科学硕士学位，并于1998年获得犹他大学计算机科学博士学位。从1996年到1999年，他是美国宇航局NASA艾姆斯研究中心的研究科学家。他的主要研究兴趣是科学可视化和计算机图形学。沈教授是美国国家科学基金会杰出年轻计划主持人奖和美国能源部的杰出年轻计划主持人奖获得者。他还在俄亥俄州立大学计算机科学与工程系获得了两次杰出教学奖，以及2014年Joel和Ruth Spira杰出教学奖。 他目前是IEEE SciVis会议指导委员会成员。
    Title: Information Theory for Data Analysis and Visualization (2018.7.25 14:00-17:00)
    Abstract: Originally proposed by Claude Shannon and Norbert Wiener in the late 1940s, information theory has been used to study the fundamental limit to reliably transmit messages through a noisy communication channel. To date, information theory has made a profound impact on many areas not only in communication, but also in physics, psychology, and art, just to name a few. In this tutorial, I will take an information-theoretic viewpoint to consider the process of visual analysis as a channel, a visual communication channel, that attempts to communicate the information in the data to the viewer. I will start by introducing some fundamental information-theoretic metrics for quantification of information in a data set. Then I will describe a variety of applications of information theory for scientific and information visualization. This tutorial is designed for general audience, and especially suitable for students and researchers who are looking for new research topics and wish to broaden their research horizon.
    Bio: 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, and 2014 Joel and Ruth Spira Excellence in Teaching Award. He is currently a member of steering committee for IEEE SciVis conferences.

    课程教师: 刘世霞（清华大学）
    题目: 可解释机器学习 (2018.7.25 14:00-16:00)
    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。本报告将介绍我们提出的机器学习模型可视分析框架。该框架跳出传统可视分析“先分析再可视化”的单一方向分析机制，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及其输出结果，分析、诊断并不断完善机器学习模型。为用户选择、利用及改进机器学习模型提供技术依据。最后，结合具体的应用实例，如集成学习模型和深度学习模型分析等，介绍我们基于该框架研制开发的可视分析技术。
    课程教师简介: 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/。
    Title: Explainable Machine Learning With Interactive Visualization (2018.7.25 16:00-18:00)
    Abstract: Machine learning has demonstrated being highly successful at solving many real-world applications ranging from information retrieval, data mining, and speech recognition, to computer graphics, visualization, and human-computer interaction.. However, most users often treat the machine learning model as a “black box” because of its incomprehensible functions and unclear working mechanism. Without a clear understanding of how and why the model works, the development of high-performance models typically relies on a time-consuming trial-and-error procedure. This talk presents the major challenges of interactive machine learning and exemplifies the solutions with several visual analytics techniques and examples, including model understanding and diagnosis.
    Bio: Shixia Liu is an associate professor at Tsinghua University. Her research interests include visual text analytics, visual social analytics, visual behavior analytics, graph visualization, and tree visualization. Before joining Tsinghua University, she worked as a lead researcher at Microsoft Research Asia and a research staff member at IBM China Research Lab. Shixia is one of the Papers Co-Chairs of IEEE VAST 2016 and 2017. She is an associate of IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Big Data, and is on the editorial board of Information Visualization. She was the guest editor of ACM Transactions on Intelligent Systems and Technology and Tsinghua Science and Technology. She was the program co-chair of PacifcVis 2014 and VINCI 2012. Shixia was in the Steering Committee of VINCI 2013. She is on the organizing committee of IEEE VIS 2015 and 2014. She is/was in the Program Committee for CHI 2018, InfoVis 2015, 2014, VAST 2015, 2014, KDD 2015, 2014, 2013, ACM Multimedia 2009, SDM 2008, ACM IUI 2011, 2009, PacificVis 2008, 2009, 2010, 2011, PAKDD 2013, VISAPP 2012, 2011, VINCI 2011.