Currently I am a Research Scientist at Bosch Research North America (Sunnyvale, CA). Prior to that, I obtained PhD in Computer Science in Hong Kong University of Science and Technology , supervised by Prof. Huamin Qu, and Bachelor's degree in Computer Science from Zhejiang University.

  • E-mail: panpan.xu at, xpp2007 at
  • Research interest: visual analytics, data mining
  • Links:
  • News: Two papers accepted to VAST 18 TVCG track. One paper got best paper award.
  • News: Event sequence visualization paper accepted to VAST 17 TVCG track. (video and paper and supplementary material)


(Full list: Google Scholar)

* students I supervised.

TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis
IEEE Transactions on Visualization and Computer Graphics (VAST 2018, to appear)
[Best Paper Award]
Dongyu Liu*, Panpan Xu, Liu Ren

We model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which simultaneously slices the data into homogeneous partitions and extracts the latent patterns for each partition for comparison and visual summarization.

ViBR: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle
IEEE Transactions on Visualization and Computer Graphics (VAST 2018, to appear)
Gromit Yeuk-Yin Chan*, Panpan Xu, Zeng Dai, Liu Ren

We propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. An efficient algorithm based on locality sensitive hashing (LSH) further enables interactive analysis of large bipartite graphs.

Sequence Synopsis: Optimize Visual Summary of Temporal Event Data
IEEE Transactions on Visualization and Computer Graphics (VAST 2017)
Yuanzhe Chen*, Panpan Xu, Liu Ren

We propose a novel event sequence data summarization technique based on the minimum description length (MDL) principle. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in the display. The algorithm enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. It is applied to two real world use cases: fault development path analysis in vehicles and user click stream analysis in software application logs. A Metadata Collection about IEEE Visualization (VIS) Publications
IEEE Transactions on Visualization and Computer Graphics 2017
Petra Isenberg, Florian Heimerl, Steffen Koch, Tobias Isenberg, Panpan Xu, Chad Stolper, Michael Sedlmair, Jian Chen, Torsten Möller, John Stasko

Introduce a dataset that contains information on IEEE Visualization (IEEE VIS) publications from 1990-2015 and several sample visualizations. The dataset includes a variety of information about each paper including title, authors, DOI, etc., as well as a list of the citations to other previous VIS papers.

ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories
IEEE Transactions on Visualization and Computer Graphics (VAST 2016)
[Best Paper Honorable Mention Award]
Panpan Xu, Honghui Mei*, Liu Ren, Wei Chen

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects.

Interactive Visual Co-cluster Analysis of Bipartite Graphs
IEEE Pacific Visualization Symposium (PacificVis)}, 2016
Panpan Xu, Nan Cao, Huamin Qu, John Stasko

Examples of bipartite relations include the votes from legislators for the passage of bills and amendments, the involvement of researchers in various topics, and the affiliation of individuals with different social groups. This paper introduce a visual analytic system for co-cluster analysis in bipartite graphs.

Visual Analysis of Topic Competition on Social Media
IEEE Transactions on Visualization and Computer Graphics (VAST 2013)
Panpan Xu, Yingcai Wu, Enxun Wei, Tai-Quan Peng, Shixia Liu, Jonathan J.H. Zhu, Huamin Qu

How do various topics compete to attract public attention when they are spreading on social media? What roles do opinion leaders such as mass media, political figures and grassroots play in the rise and fall of various topics? In this study, we combine quantitative modeling and interactive visualization to gain insight into the temporal dynamics of topic competition on social media (e.g.,Twitter).

Visual Analysis of Set Relations in a Graph
Computer Graphics Forum (Eurovis 2013)
Panpan Xu, Fan Du, Nan Cao, Conglei Shi, Hong Zhou, Huamin Qu

In many cases, graph data not only records the relationship among people, but also the various items they are related to (e.g., interested topics or purchased goods). We design visualizations that can help to study if people linked together have similar items of interest, and introduced a novel set visualization technique to show the overlap of their interests.

Edge Bundling in Information Visualization
Tsinghua Science and Technology, 2013
Hong Zhou, Panpan Xu, Xiaoru Yuan and Huamin Qu

Edge bundling techniques reduce visual clutter and enhance the patterns in node-link graph visualizations and parallel coordinates plot, where edges (lines) act as an important visual primitive to convey information. This work is a survey on the existing edge bundling algorithms.

RankExplorer: Visualization of Ranking Changes in Large Time Series Data
IEEE Transactions on Visualization and Computer Graphics (InfoVis 2012)
Conglei Shi, Weiwei Cui, Shixia Liu, Panpan Xu,
Wei Chen, and Huamin Qu

Ranking changes appear in many applications. This paper presents a scalable solution to visualize the ranking change among a large number of items such as search words, which are ranked by their popularity and constantly changes overtime.


PhD, Computer Science

  • Vis Lab, Hong Kong University of Science and Technology

Visiting Student

B.S., Computer Science

Professional Activities

Program Committee

  • IEEE VIS (VAST) Papers, 2017 - 2018
  • IEEE Pacific Visualization Symposium (PacificVis) Papers, 2017 - 2019
  • IEEE Pacific Visualization Symposium (PacificVis) Notes, 2016
  • International Symposium on Visual Computing (ISVC), 2018
  • ChinaVis, 2017 - 2018


  • IEEE VIS (InfoVis and VAST)
  • IEEE Transactions on Visualization and Computer Graphics
  • The EG/VGTC Conference on Visualization (EuroVis)
  • IEEE Pacific Visualization Symposium (PacificVis)
  • IEEE Computer Graphics and Applications Magazine

Panpan Xu, 2018