(Geo) VIZ Resources – Deep Learning Garden

This page provides some resources about VIZ or GeoVIZ.

(Stay tuned, as I will update the content on this page while I plow and grow in my deep learning garden:))

Table of Contents

(Geo) VIZ Scholars/Groups

Alan M. MacEachren (Google Scholar, GeoVISTA center, GeoVISTA center publications)
Ben Shneiderman (Google Scholar)
Niklas Elmqvist (Google Scholar) — website (a very good one, has some pretty good posts)
Tamara Munzner (Google Scholar) — website (Talks, See also many course slides posted online.)

Selected Talks from Prof. Tamara Munzner (see complete list of Talks HERE).

- - - - Visualization: From Pixels to Insight (2007) — PDF
      - Mathematics For Visualization versus Visualization Of Mathematics (2007) — PDF
      - Visualization Principles (2012) — PDF
      - Information Visualization: Frameworks/Models (2009) — PDF
    - - A Nested Model for Visualization Design and Validation (2009) — PDF
      - When To Walk Away: Questions To Ask In Infovis Projects (2007) — PDF
      - Evaluation, When and How (2013) — PDF
      - Visualization: Why It’s Tricky, Where To Start (2014) — PDF
      - Is Big Data Visualization Possible? (2014) — PDF
      - Data Visualization Pitfalls to Avoid (2017, 50 min version, 48 slides) — PDF
      - Data Visualization as a Driver for Visual Cognition Research (2017) — PDF
      - Characterization of Information Visualization Systems (2018) — PDF
      - Entry Points to Visualization: Different Methods for Different Problems (2019) — PDF
      - Making Data Accessible Through Visualization (2019) — PDF
      - TBA
    - Information Visualization Mini-Course
      - 5 hour version
        
        Part 1: Design, Models, Perception. Slides: pdf
        
        Part 2: Color, Space, Statistical Graphics. Slides: pdf
        
        Part 3: Multiples, Navigation, Focus+Context. Slides: pdf
        
        Part 4: High Dimensional, Graphs and Trees, User Studies. Slides: pdf
      - 6 hour version
        
        Part 1: Design, Models, Perception. Slides: pdf
        
        Lecture 1 HTML, pdf
        
        Lecture 2 HTML, pdf
        
        Lecture 3 HTML, pdf

Shixia Liu (Google Scholar)

(There are videos and demos for some VIZ tools on Dr. Shixia Liu’s home page under Publications)

Some selected papers from Dr. Shixia Liu’s research group:

Liu, S., Wang, X., Collins, C., Dou, W., Ouyang, F., El-Assady, M., … & Keim, D. (2018). Bridging text visualization and mining: A task-driven survey. IEEE transactions on visualization and computer graphics. (PDF, tool — a visualization tool that is part of a survey of text visualization & mining)

Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48-56.

Jiang, L., Liu, S., & Chen, C. (2018). Recent research advances on interactive machine learning. Journal of Visualization, 1-17.

Liu, S., Chen, C., Lu, Y., Ouyang, F., & Wang, B. (2019). An interactive method to improve crowdsourced annotations. IEEE transactions on visualization and computer graphics, 25(1), 235-245.

Liu, S., Andrienko, G., Wu, Y., Cao, N., Jiang, L., Shi, C., … & Hong, S. (2018). Steering data quality with visual analytics: The complexity challenge. Visual Informatics.

Liu, M., Shi, J., Li, Z., Li, C., Zhu, J., & Liu, S. (2017). Towards better analysis of deep convolutional neural networks. IEEE transactions on visualization and computer graphics, 23(1), 91-100.

Chris North (Google Scholar)
Georgia Tech Visualization Lab | GT VIS (people)
- Alex Endert (Google Scholar) – website
- Thanks NSF!
  Endert’s NSF proposal titled “Detecting and Mitigating Biased Visual Analysis Strategies” has been funded. With generous NSF support, we will develop automated methods to detect and mitigate potentially biased visual data analysis strategies.

- Polo Chau (Google Scholar) – website

Daniel Keim (Google Scholar)
Dominik Sacha (Google Scholar)

Selected papers:

Sacha, D., Sedlmair, M., Zhang, L., Lee, J. A., Weiskopf, D., North, S., & Keim, D. (2016, August). Human-centered machine learning through interactive visualization. ESANN. (PDF)

Jürgen Bernard (Google Scholar)

Selected papers:

Bernard, J., Hutter, M., Zeppelzauer, M., Fellner, D., & Sedlmair, M. (2018). Comparing visual-interactive labeling with active learning: An experimental study. IEEE transactions on visualization and computer graphics, 24(1), 298-308. (PDF)