GAMES Webinar 2024 – 324期(多场景下可视分析系统设计) |Talk+Panel形式

【GAMES Webinar 2024-324期】(可视化专题-多场景下可视分析系统设计|Talk+Panel形式)

详细日程:2024年5月23号 20:00-21:30(北京时间)

20:00-20:15   Stepping Into the Era of Explainable Quantum Computing(阮劭伦Singapore Management University)

20:15-20:30   VAID: Indexing View Designs in Visual Analytics System(应璐,浙江大学)

20:30-20:45   LiberRoad: Probing into the Journey of Chinese Classics through Visual Analytics(郭宇涵,北京大学)

20:45-21:00   图可视化的智能分析与设计方法研究(宋思程华东师范大学)

21:00-21:30   圆桌研讨


报告嘉宾:阮劭伦(Singapore Management University)


报告题目:Stepping Into the Era of Explainable Quantum Computing


Shaolun Ruan is currently a Ph.D. candidate in School of Computing and Information Systems at Singapore Management University (SMU). His work focuses on developing novel graphical representations that enable a more effective and smoother analysis for humans using machines. His work focuses on handling complex and abstract domain problems like quantum computing and bioinformatics, leveraging the methods from Data Visualization and Augmented Reality. He received his bachelor’s degree from the University of Electronic Science and Technology of China (UESTC) in 2019. For more information, kindly visit


Quantum computing has made significant progress in recent years. The rapid growth in the quality and quantity of quantum computers by leading IT companies such as IBM, Google and Amazon makes the potential quantum advantages increasingly realistic. However, previous research has determined that grasping abstract concepts in quantum computing remains challenging. For example, it is difficult for quantum computing developers and researchers to understand the function of each quantum gate and the final measured probability of each basis state. In this task, Shaolun Ruan will introduce how to make users confident in the workflow of quantum computing by visualizing the inner workings of quantum circuits. Specifically, the talk will cover visual interpretability across different directions like static quantum circuits, quantum neural networks, quantum states, and the noise mitigation hidden in the IBM Quantum platform.




报告题目:VAID: Indexing View Designs in Visual Analytics System


Lu Ying (应璐) is a fourth-year PhD student in the School of Computer Science and Technology at Zhejiang University, under the supervision of Prof. Yingcai Wu. Her research primarily focuses on data storytelling and glyph-based visualization. She is dedicated to integrating AI techniques into visualization to simplify the creation process. Additionally, she conducts foundational and advanced research in visual analytics, with significant achievements in intelligent visualization generation and information visualization storytelling. She has published four first-author papers in top-tier journals and conferences, including IEEE TVCG, IEEE VIS, and ACM CHI.


Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.




报告题目:LiberRoad: Probing into the Journey of Chinese Classics through Visual Analytics


Yuhan Guo is a first-year Ph.D. student in the School of Intelligence Science and Technology, at Peking University. She received a B.S. degree in intelligence science and technology from Peking University in 2023. Her research interests include text visualization and visualization for humanities. Her recent research focuses on visual analytics of provenance data and designing interactive visual interfaces for human-AI collaboration. She has published papers in top visualization venues such as IEEE VIS and IEEE TVCG.


Books act as a crucial carrier of cultural dissemination in ancient times. The circulation of Chinese classics has played a significant role in the spreading of Chinese culture and its exchanges with other regions. While traditional humanities studies focus on specific books or collections, interdisciplinary efforts can allow a holistic view and inspire new insights. I will introduce our collaboration with humanities researchers on developing a visual analytics system to inspect the overseas circulation of Chinese classics from China to Japan. Specifically, I will introduce a novel visualization approach we designed to cope with the significant uncertainty in the spatial data, as well as the collaborative experience to develop the visual analytics system.






Sicheng Song (宋思程) is a 5th-year Ph.D candidate in the School of Computer Science and Technology at East China Normal University (ECNU). He is under Prof. Changbo Wang, and Prof. Chenhui Li’s supervision. He works on the research of graph visualization, reverse visualization, and AI4VIS. He has published his works in top-tier venues such as IEEE TVCG, ACM CHI, and IEEE VR. He has also served as a reviewer for ACM CIKM, and IEEE VIS.


In the rapidly evolving landscape of computer technology, graph visualization faces an array of complex and pressing challenges, including multi-source heterogeneity, variable data quality, time sensitivity, and the need for advanced interactivity. Tackling these issues demands intelligent and sophisticated design and implementation support. My doctoral research addresses these challenges through three interrelated advancements: data extraction techniques, style transfer processes, and a automatic question-answering system tailored to graph visualization. This work not only expands the theoretical understanding of graph visualization but also establishes a comprehensive suite of practical solutions, enhancing graph functionality, aesthetics, and user engagement.



Chengtao Ji received his M.S. and Doctoral degree from Sichuan University and Groningen University in 2014 and 2018, respectively. He is currently an assistant professor in Xi’an Jiaotong-Liverpool University, China. His research interests include data mining, complex network analysis, and visual analytics.

Yunzhe Wang received her M.S. and PhD in Computer Science from The University of Hong Kong and The Hong Kong Polytechnic University in 2014 and 2020, respectively. She is currently a lecturer in Suzhou University of Science and Technology, China. Her research interests include data science, social networks, and visual analysis. She has published paper on top conferences and journals including Computer Graphics Forum and ACM Transactions on Knowledge Discovery from Data and so on. She received ICCI*CC Best Paper Award in 2015, SIGGRAPH Asia Sym. Vis. Best Paper Award in 2017 and Jiangsu Innovation and Entrepreneurship Ph.D. from World Prestigious Universities Award in 2021. She now hosts a project supported by the Young Scientists Fund of the National Natural Science Foundation of China.

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