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UW Graphics Lab members

Welcome to the University of Wisconsin-Madison Computer Graphics home page!
The UW Graphics Group is led by Profs. Mike Gleicher and Eftychios Sifakis.

[previous lab photo: 2014]

Research Topics

Click on the thumbnail to learn more about current projects going on in the lab!

UW Graphics Lab at SIGGRAPH 2015

We recently returned from SIGGRAPH 2015 in Los Angeles, California.

We presented our work on simulating plastic surgery procedures and sketching of space-time curves for character animation:

Nathan Mitchell, Court Cutting, and Eftychios Sifakis. GRIDiron: An Interactive Authoring and Cognitive Training Foundation for Reconstructive Plastic Surgery Procedures. ACM Transactions on Graphics, 34(4), pp. 43:1–43:12, 2015.

Martin Guay, Remi Ronfard, Michael Gleicher, and Marie-Paule Cani. Space-Time Sketching of Character Animation. ACM Transactions on Graphics, 34(4), pp. 118:1–118:10, 2015.

We also presented a poster, where Danny’s work was selected as a finalist and won first place in the Student Research Competition! Many congratualations to Danny, who will join the lab as a graduate student in the upcoming semester.

Daniel Rakita, Tomislav Pejsa, Bilge Mutlu, and Michael Gleicher. Inferring Gaze Shifts from Captured Body Motion.  ACM SIGGRAPH 2015 Posters, n. 77, pp. 77:1, 2015.



UW Graphics Lab at VIS 2014

We just returned from the IEEE VIS 2014 conference in Paris, France.

We presented two full papers:

Michael Correll and Michael Gleicher. Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. (InfoVis track)

Eric Alexander, Joe Kohlmann, Robin Valenza, Michael Witmore, and Michael Gleicher. Serendip: Topic Model-Driven Visual Exploration of Text Corpora. (VAST track)

We also presented three workshop papers:

Michael Correll, Eric Alexander, Danielle Albers Szafir, Alper Sarikaya, and Michael Gleicher. “Navigating Reductionism and Holism in Evaluation.” (BELIV 2014: Beyond Time and Errors – Novel Evaluation Methods for Visualization)

Michael Correll and Michael Gleicher. “Data Are Secular, Not Sacred.” (DECISIVe : Dealing With Cognitive Biases in Visualizations)

Michael Gleicher. “Towards Comprehensible Predictive Modeling.” (Workshop on Visualization for Predictive Analytics)

And Michael Gleicher was a panelist for “Bridging the Gap: Translating Perception and Cognition Expertise into Visualization Research and Practice.”


The Graphics Lab recently returned from the IS&T 22nd Color and Imaging  Conference (CIC 2014) in Boston, MA. We presented two papers:

Danielle Albers Szafir, Maureen Stone, and Michael Gleicher. Adapting Color Difference for Design.

Maureen Stone, Danielle Albers Szafir, and Vidya Setlur. An Engineering Model for Color Discriminability as a Function of Size.

in conjunction with Tableau Research. “Adapting Color Difference for Design” was voted Best Student Paper. It was a wonderful opportunity to present our visualization work to and gain insights from the color science community!


UW Graphics Lab at VSS 2014

The graphics lab recently returned from the Vision Science Society’s Annual Meeting (VSS 2014) in St. Pete’s Beach, FL. We presented one poster

Danielle Albers, Michael Correll, Michael Gleicher, and Steve Franconeri. Ensemble Processing of Color and Shape: Beyond Mean Judgments.

in conjunction with the Northwestern Visual Cognition Lab. It was a wonderful opportunity to present our visualization work with and gain insights from the vision science community!


UW Graphics Lab at CHI 2014

In May, the graphics lab participated in the 2014 SIGCHI Conference on Human Factors in Computing Systems (CHI 2014) in Toronto, Ontario. We presented:

Danielle Albers, Michael Correll, and Michael Gleicher. Task-Driven Evaluation of Aggregation in Time Series Visualization.

It was a great opportunity to explore what’s happening in both the visualization and broader HCI community!


The graphics lab just got back from the Eurographics Conference on Visualization (EuroVis ’14) in Swansea, Wales, U.K.  We presented one paper:

Alper Sarikaya, Danielle Albers, Julie C. Mitchell, and Michael Gleicher.  Visualizing Validation for Protein Surface Classifiers.

We were happy to see old colleagues and meet new ones in the visualization community!


The Explainers effort is trying to develop methods that help people build understanding of high dimensional data sets. We attempt to find models of the data that are easy to comprehend, and more likely to lead the viewer to develop theories or understanding. This involves exploring tradeoffs between the typical machine learning and statistics goals for models (accurately and efficiently representing data, or making predictions from it) and other properties that make the models more usable by people (e.g. simplicity, familiarity, parsimony).

The initial paper considers trying to create simple “explanations” of binary concepts (like “Comedic-ness” or “like-Paris-ness”) in terms of high dimensional data. It adapts machine learning classification techniques to account for tradeoffs between various types of performance and understandability :

Michael Gleicher. Explainers: Expert Explorations with Crafted Projections. IEEE Transactions on Visualization and Computer Graphics, 19 (12) 2042-2051. Proceeding VAST 2013. (link) (doi) (Best paper honorable mention)

The supplementary material from the paper is a good place to look at examples (much better than the paper). In fact, the simplest example (Figure 0) is probably the best place to start.

Many of our example problems and motivations come from the Visualizing English Print project.


UW Graphics Lab at VIS2013

The graphics lab just got back from the IEEE VIS Conference in Atlanta, GA. We presented three papers:

Michael Gleicher. Explainers: Expert Explorations with Crafted Projections.

Michael Gleicher, Michael Correll, Christine Nothelfer, and Steve Franconeri. Perception of Average Value in Multiclass Scatterplots

Adrian Mayorga and Michael Gleicher. Splatterplots: Overcoming Overdraw in Scatter Plots.

Four posters:

Michael Correll and Michael Gleicher. Error Bars Considered Harmful.

Danielle Albers, Alper Sarikaya, and Michael Gleicher. Lightness Constancy in Surface Visualization.

Eric Alexander, Joe Kohlmann, Robin Valenza, and Michael Gleicher. Serendip: Turning Topics Back to the Text.

Alper Sarikaya, Danielle Albers, and Michael Gleicher. Understanding Performance of Protein Structural Classifiers.

Two of our students (Danielle Albers and Michael Correll) were participants in the 2013 VIS Doctoral Colloquium. In addition Lightness Constancy in Surface Visualization won Best Poster in the SciVis track, and Explainers: Expert Explorations with Crafted Projections. was an Honorable Mention paper in the VAST track.

Congratulations to all attendees and accepted papers!


Knowledge Repository

The wordpress blog graphics.cs.wisc.edu/WP/knowledge has been created to allow people to post a searchable and indexed list of experience, tips, tricks, and opinions on different frameworks, languages, and environments for making graphics projects. If you are not already a member you will need to be approved by an admin to view the posts.



Below is a link to the latest binaries of Splatterplots as of 1/18/2013

I’ve also uploaded all of the datasets to V:\graphics\graphics-data\adrm\SplatterplotsData