The 10th IEEE Symposium on Large Data Analysis and Visualization
in conjunction with IEEE VIS 2020, Salt Lake City, Utah, USA, October 25, 2020


Sunday, October 25, 2020
(Mountain Time, USA)

8:00am – 8:10am Opening Remarks (Youtube live stream)
8:10am – 9:00am

Keynote Presentation (Youtube live stream)

Large-Scale Visual Analytics: A Reflection on Methodology and Practice
Professor Huamin Qu, Hong Kong University of Science and Technology Details
9:00am – 9:30am

Best Paper (Youtube live stream)

(Session Chair: Hongfeng Yu)
  • Foveated Encoding for Large High-Resolution Displays
    Florian Frieß, Matthias Braun, Valentin Bruder, Steffen Frey, Guido Reina,Thomas Ertl
9:30am – 10:00am Break
10:00am – 11:30pm

Session: In Situ (Youtube live stream)

(Session Chair: Ming Jiang)
11:30am – 12:00 pm Break
12:00pm – 1:30pm

Session: Rendering and Displays (Youtube live stream)

(Session Chair: Markus Hadwiger)
1:30pm – 2:00pm Break
2:00pm – 3:20pm


Technical Advances in the Era of Big Data Analysis and Visualization (Youtube live stream)
Moderator: Julien Tierny
Panelists: Jim Ahrens, Janine Bennett, Andreas Gerndt, Ken Moreland, Manish Parashar
The year 2020 marks the tenth edition of the IEEE Symposium on Large Data Analysis and Visualization. At the occasion of this milestone, we would like to take a step back to contemplate the main accomplishments of our community over the last decade and present the main ongoing efforts in the land of large data analysis and visualization. For this, we gathered a panel of leading experts in the field, who will present their feedback on progresses in the last ten years and ongoing efforts on the topic. Future directions to take as a community, and how to organize research efforts to address them, will also be an important aspect of the panel.
3:20pm – 3:30pm Best Paper Awards
Closing Remarks


Large-Scale Visual Analytics: A Reflection on Methodology and Practice
Professor Huamin Qu, Hong Kong University of Science and Technology, Hong Kong

In the past decade, the VisLab at the Hong Kong University of Science and Technology has closely worked with industry to develop large-scale visual analytics systems for different application domains, especially E-Learning, Urban Computing, Smart Manufacturing, and FinTech. These systems have been deployed to more than 10 companies and about 20 papers related to these systems have been published in IEEE VAST conferences. During this process, we have accumulated experience and learned lessons. In this talk, I will introduce some representative works and share my reflection on the methodology and practice of developing large-scale visual analytics systems. I will especially talk about the limitations of the current approaches including the ad-hoc nature of the methodology and the overly complicated visualization designs and systems, and provide my thoughts on how these can be improved with the help of machine learning and also technology from software engineering. 


Prof. Huamin Qu is the Director of Interdisciplinary Programs Office (IPO) and a professor in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST). His main research interests are in visualization and human-computer interaction, with focuses on urban informatics, social network analysis, E-learning, text visualization, and explainable artificial intelligence (XAI). He is currently an associate editor of Computer Graphics Forum (CGF), and was an associate editor of IEEE Transactions on Visualization and Computer Graphics (TVCG), a paper co-chair for IEEE VIS’14 (SciVis), VIS’15 (SciVis) and VIS’18 (VAST). He has served on the steering committees of IEEE PacificVis, IEEE VAST, and IEEE VIS. Prof. QU’s research has been recognized by many awards including 11 Best paper/Honorable Mention awards, IBM Faculty Award, Distinguished Collaborator Award from Huawei Noah’s Ark Lab, and AI 2000 Most Influential Scholar Award (for Contributions to the field of Visualization between 2009 and 2019). He has graduated 28 PhD students.


The LDAV posters session will take place virtually with IEEE VIS Poster Session via iPosters.