Keynote Speaker: Jean-Daniel Fekete, INRIA
    Title: Progressive Data Analysis: a new computation paradigm for scalability in exploratory data analysis
    Abstract: Exploring data requires a short feedback loop, with a latency of at most 10 seconds because of human cognitive capabilities and limitations. When data becomes large or analyses become complex, sequential computations can no longer be completed in a few seconds and interactive exploration is severely hampered. This talk will describe a novel computation paradigm called Progressive Data Analysis that brings at the programming language level the low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems — including visual analytics — from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory analytics systems. I will describe the new paradigm, report on novel experiments showing that human can cope effectively with progressive systems, and show demos using a prototype implementation called ProgressiVis, explain the requirements it implies through exemplar applications, and present opportunities and challenges ahead, in the domains of visualization, visual analytics, machine-learning, and databases.
    Bio: Jean-Daniel Fekete is Senior Research Scientist (DR1) at INRIA, Scientific Leader of the INRIA Project Team AVIZ that he founded in 2007. He received his PhD in Computer Science in 1996 from Université Paris-Sud. From 1997 to 2001, he joined the Graphic Design group at the Ecole des Mines de Nantes that he led from 2000 to 2001. He was then invited to join the Human-Computer Interaction Laboratory at the University of Maryland in the USA in 2001-2002. He was recruited by INRIA in 2002 as a confirmed researcher and became Senior Research Scientist in 2006. In 2015, he was on Sabbatical at the Visualization and Computer Graphics group at NYU-Poly, and at the Visual Computing Group at Harvard. His main Research areas are Visual Analytics, Information Visualization and Human Computer Interaction. He published more than 150 articles in multiple conferences and journals, including the most prestigious in visualization (TVCG, InfoVis, EuroVis, PacificVis) and Human-Computer Interaction (CHI, UIST). He is the chair of the IEEE Information Visualization Conference Steering Committee, member of the IEEE VIS Executive Committee, member of the Eurographics EuroVis Steering Committee, and member of the Eurographics publication board. He is also an ACM Distinguished Speaker. Jean-Daniel Fekete was the General Chair of the IEEE VIS Conference in 2014, the first time it was held outside of the USA in Paris, Associate Editor of the IEEE Transactions on Visualization and Computer Graphics (TVCG) 2011-2015, the President of the French-Speaking HCI Association (AFIHM) 2009-2013, he was Conference Chair of the IEEE InfoVis Conference in 2011, Paper Co-Chair of the IEEE Pacific Visualization conference in 2011. Jean-Daniel Fekete is a member of the Association for Computer Machinery (ACM) and Senior Member of the IEEE.

    报告人：莫则尧，北京应用物理与计算数学研究所
    题目：超级计算与可视分析
    摘要：可视分析是重大科学与工程超级计算研究的重要组成部分。随着超级计算规模和物理建模精细度的大幅提升，对可视分析提出了更深入的研究要求。本报告从这个角度出发，结合实际应用和研究现状，阐述亟待解决的数据模型、可视分析流程、多物理数据表现建模与方法、应用软件可视分析界面快速定制等方面的实际应用问题。
    个人简介： 莫则尧，北京应用物理与计算数学研究所副所长，研究员，曾获国家杰出青年科学基金、冯康科学计算奖、中国科协“求是”杰出青年奖（实用工程奖）、和全国五一劳动奖章，入选国家百千万人才工程、有突出贡献中青年专家，作为主要完成人曾获国家科技进步奖特等奖和一等奖2项、军队科技进步奖一等奖5项，现为全国工业与应用数学学会副理事长，全国计算机协会高性能计算专业委员会副主任，高性能计算国家重点实验室、计算机体系结构国家重点实验室学术委员会委员。