祝贺！iDVX 实验室 获 IEEE VIS 2023「最佳论文奖」及「最佳论文提名奖」

北京时间8月23日，IEEE VIS (CCF-A) 公布了 2023 年度论文获奖名单。iDVX 实验室荣获 “最佳论文” 及 “最佳论文提名” （与美国西北大学合作） 两项论文大奖。原文链接：https://ieeevis.org/year/2023/info/awards/best-paper-awards （或点击文后「阅读原文」查阅官方完整信息）。

IEEE VIS 是数据可视化领域唯一公认的顶级国际会议。这是 IEEE VIS 大会创办 33 周年以来第一次将最佳论文奖颁发给完全由亚洲高校作者主导完成的科研论文，这也是中国大陆地区第一次获得该会议的最佳论文奖项。 除此之外，此次获得的最佳论文提名奖，是 iDVX 实验室第4次获得最佳论文提名，前3次 分别为IEEE VIS 2014年1项（针对社交媒体中谣言扩散现象的可视分析系统） 及 IEEE VIS 2022年2项（可视化叙事中的交互设计范式研究 与 智能可视化象形图设计创作工具与技术）。IDVX 实验室将持续在数据可视化及信息传达设计领域不断突破与创新。

最佳论文奖

Affective Visualization Design:
Leveraging the Emotional Impact of Data
Xingyu Lan, Yanqiu Wu, Nan Cao

Justification

“In an area that has only recently been recognized for its importance, this paper stands out in providing a comprehensive review and evaluation of emotionally affective visualization design. With its systematic structuring of 170 surveyed papers and designs, along with a nuanced discussion of affective design form and motivation, it provides an important contribution to the community. It moves us beyond a collection of disparate design examples by establishing a foundational design space and framework relating design technique, thematic genre, and task. In doing so, it provides an important step for future work to evaluate and improve affective design in visualization.”

曹楠是同济大学设计创意学院副院长，同济大学长聘教授，同济大学智能大数据可视化实验室主任。在此之前，他是美国 IBM 沃森研究院研究员。曹楠的主要研究方向是数据可视化及信息传达设计。曾获得 IBM 杰出技术成就奖，IBM 杰出研究成就奖，微软最有价值专家，AI 2000 最有影响力学者提名, 福布斯中国智能工业设计师 Top 20 等荣誉。

最佳论文提名奖

InnovationInsights: A Visual Analytics Approach for Understanding the Dual Frontiers between Science and Technology

Yifang Wang*, Yifan Qian*,Xiaoyu Qi⁺,
Nan Cao⁺, Dashun Wang*

* 美国西北大学
凯洛格管理学院科学与创新中心

⁺同济大学
智能大数据可视化实验室

Abstract

Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in propelling real-world applications and technological progress. The increasing availability of large-scale datasets tracing scientific publications and patented inventions and the complex interactions among them offers us new opportunities to explore the evolving dual frontiers of science and technology at an unprecedented level of scale and detail. However, we lack suitable visual analytics approaches to analyze such complex interactions effectively. Here we introduce InnovationInsights, an interactive visual analysis system for researchers, research institutions, and policymakers to explore the complex linkages between science and technology and to identify critical innovations, inventors, and potential partners. The system first identifies important associations between scientific papers and patented inventions through a set of statistical measures introduced by our experts from the field of the Science of Science. A series of visualization views are then used to present these associations in the data context. In particular, we introduce the Interplay Graph to visualize patterns and insights derived from the data, helping users effectively navigate citation relationships between papers and patents. This visualization thereby helps them identify the origins of technical inventions and the impact of scientific research. We evaluate the system through two case studies with experts followed by expert interviews. We further engage a premier research institution to test-run the system, helping its institution leaders to extract new insights for innovation. Through both the case studies and the engagement project, we find that our system not only meets our original goals of design, allowing users to better identify the sources of technical inventions and to understand the broad impact of scientific research; it also goes beyond these purposes to enable an array of new applications for researchers and research institutions, ranging from identifying untapped innovation potential within an institution to forging new collaboration opportunities between science and industry.

项目团队

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