Min Lu

陆 旻

Hi! I am now an associate professor at Shenzhen University. I received my Ph.D degree in Computer Science from Peking University in 2017. My research interest is generally Visualization and Visual Analytics, including: (1) design intuitive visualization, e.g., Winglets, Sticky Links, Interaction+. (2) understand visual designs, e.g., Visual Information Flow in Infographics, JND Modeling in Charts; (3) address urban problems in visual analytic manner, e.g., a series of trajectory analysis methods.

: ) I am looking for graduate students who share a strong interest with what I am doing! Please contect me with your resume reflecting your passion.

Google Scholar Github
Contact Me: lumin.vis (at) gmail.com
Updates

  • 2024.Api

    StickyLinks won Best Paper Honorable Mention award at PacificVis 2024!

  • 2023.Dec

    Our paper StickyLinks is accepted in PacificVis 2024 TVCG track.

  • 2022.Nov

    Meet our second lovely baby Xinye :)

  • 2022.Jul

    Our paper HiTailor is accepted in IEEE VIS 2022.
Selected Projects

A Versatile Collage Visualization Technique

Zhenyu Wang and Min Lu*

Arxiv
[ Paper (4.6 MB)] [ Project Homepage]

We introduce a versatile image-space collage technique designed to pack geometric elements into a given shape, and demonstrate its diverse visual expressiveness in various applications.

Sticky Links: Encoding Quantitative Data of Graph Edges

Min Lu*, Xiangfang Zeng, Joel Lanir, Xiaoqin Sun, Guozheng Li, Daniel Cohen-Or, and Hui Huang

PacificVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2024.
[ Paper (0.4 MB)] [ Source Code (Python)] [ My Talk at PacificVis 2024 (32.4 MB)]

In this work we propose a new edge drawing for node-link diagram, Sticky Links. Using the metaphor of stickiness, sticky links can be used for expressive edge quantatitive encoding.

Enhancing Static Charts with Data-driven Animations

Min Lu*, Noa Fish, Shuaiqi Wang, Joel Lanir, Daniel Cohen-Or and Hui Huang

TVCGIEEE Transactions on Visualization and Computer Graphics, 2022.
[ Paper (1.82 MB)] [ Project Homepage ] [ Source Code (Javascript)]

In this work we propose data-driven animation to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of data, specifically of non-temporal data.

Modeling Just Noticeable Differences in Charts

Min Lu*, Joel Lanir, Chufeng Wang, Yucong Yao, Wen Zhang, Oliver Deussen and Hui Huang

InfoVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2021.
[ Paper (2.1 MB)] [ Experiment Data ] [ My Talk at VIS 2021 ]

In this work, we model the perception of Just Noticeable Differences (JNDs) in charts with two variables (intensity and distance) and find that JND grows as the exponential function.

Exploring Visual Information Flows in Infographics

Min Lu*, Chufeng Wang, Joel Lanir, Nanxuan Zhao, Hanspeter Pfister, Daniel Cohen-Or and Hui Huang

CHI, Proceedings of the 2020 CHI conference on human factors in computing systems, 2020.
[ Paper (2.0 MB) ] [ InfoVIF dataset ] [ Demo Video (MP4, 21.1 MB) ] [ My Talk at CHI 2020 ]

In this work, we propose Visual Information Flow (VIF) in infographics and present an framework to extract it in a wild dataset InfoVIF, by which the VIF design patterns are explored.

Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

Min Lu*, Shuaiqi Wang, Joel Lanir, Noa Fish, Yang Yue, Daniel Cohen-Or and Hui Huang

InfoVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2019.
[ Paper (2.7 MB) ] [Source Code (Python) ] [ My Talk at VIS 2019 ]

In this work, we present Winglets, which are designed as a pair of dual-sided strokes, leveraging the Gestalt principle of Closure to shape the perception of the form of the clusters.