The CU VisuaLab provides an opportunity for researchers to come together to tackle innovative questions about visualization, data analytics, and computer graphics driven by real-world challenges. Below is a sample of on-going projects in the VisuaLab. For more information about these projects or others, please contact Dr. Szafir.

Modeling Color for Visualization

Color is commonly used to encode values in a visualization. However, we know little about how the complexities of visualization impact color encoding perception and design. Shape, size, shading, and even viewing device all manipulate the colors that people will see in a visualization, and subsequently their ability to effectively use that visualization. We leverage sampling-based models to better understand and predict how color manifests in different types of visualizations. How do the marks used in a visualization impact their perceptions? How can we create encodings that are robust to the ubiquity of visualizations? How can we empower designers to craft effective color encodings? This project also explores how these questions impact encodings beyond color to design tools that pair perception and automation to encourage more effective visualization.

VisuaLab Personnel: Stephen Smart, Keke Wu
Funding: National Science Foundation

Example Publications:
S. Smart, K. Wu, & D. A. Szafir. Color Crafting: Automating the Construction of Designer Quality Color Ramps. Transactions on Visualization and Computer Graphics, 26(1), 2020.

D. A. Szafir. Modeling Color Difference for Visualization Design. Transactions on Visualization and Computer Graphics, 24(1), 2018.

Human-Machine Collaborative Sensemaking

As the volume of available data increases, analytics systems must leverage automated analysis methods to make sense of data. However, these methods often remove expert knowledge from the analytic process, obscuring important patterns and leveraging black-box statistical methods. In these projects, we explore how visualizations might enable fluid collaboration between analysts and statistical methods to reintegrate people into big data processes. Our systems explore how people might leverage data synthesized across multiple sources, how statistical processes might learn from expert behavior, and how analysts can intuitively provide input into statistical processes. This research also examines how interactive visualizations may help analysts understand the processes underlying machine learning to reduce barriers to its use and interpretation in practice.

VisuaLab Personnel: David Burlinson, Tetsumichi Umada
Collaborators: Michael Paul (Paul Lab), Luke Burks, Jeremy Muesing, Nisar Ahmed (COHRINT Lab), John Hatelid (Lockheed Martin), Jed Brubaker (IDLab), & Casey Fiesler (Fiesler Lab)
Funding: National Science Foundation, U.S. Air Force

Example Publications:
H. Song & D. A. Szafir. Where's My Data? Evaluating Visualizations of Missing Data. IEEE Transactions on Visualization and Computer Graphics, 26(1) 2019.

A. Sarikaya, D. A. Szafir, & M. Gleicher. Visualizing Validation of Protein Surface Classifiers. Computer Graphics Forum, 33(3), 2014.

Scaling Up Visualizations through Vision Science

Our understanding of visualization design is conventionally based on how well people can compare pairs of points. As people face more and more data, visualization must move beyond small-scale design thinking to understand how design might support people in understanding large collections of datapoints. Drawing from psychology, this work seeks to understand how people estimate properties across collections of points in a visualization (a process known as visual aggregation) through experimentation, and how visualizations might be designed to support these judgments. The results from these efforts have driven scalable systems in domains ranging from biology to the humanities.

VisuaLab Personnel: Stephen Smart, David Burlinson

Example Publications:
D. A. Szafir, D. Stuffer, Y. Sohail, & M. Gleicher. TextDNA: Visualizing Word Usage using Configurable Colorfields. Computer Graphics Forum, 35(3), 2016. (Project Page)

D. A. Szafir, S. Haroz, M. Gleicher, & S. Franconeri. Four Types of Ensemble Coding for Data Visualizations. Journal of Vision, 16(11), 2016.

Designing for Novel Interfaces

The space of consumer display technologies is evolving rapidly. This provides people with access to displays of different shapes, sizes, and capabilities, such as mobile phones, HMDs, and smartwatches. New displays afford new opportunities for analytics tools that help people make sense of our increasingly data-driven world. This project looks at how people perceive and interact with visual information with different display technologies. We develop guidelines, techniques, and tools that effectively leverage the capabilities of these technologies to enhance the ubiquity, accessibility, and effectiveness of data analytics and immersive visual applications.

VisuaLab Personnel: Matt Whitlock, Keke Wu
Collaborators: Ethan Hanner & Jed Brubaker (CU Identity Lab); Shaun Kane (CU Superhuman Computing Lab); Catherine Diaz, Michael Walker, Christoffer Heckman (Autonomous Robotics & Perception Group); & Daniel Szafir (Iron Lab)
Funding: National Science Foundation, University of Colorado Innovative Seed Program

Project Press Release

Example Publications:
M. Whitlock, K. Wu, & D.A. Szafir. Designing for Mobile and Immersive Visual Analytics in the Field. IEEE Transactions on Visualization & Computer Graphics, 2020.

M. Whitlock, E. Hanner, J. Brubaker, S. Kane, & D. Szafir. Interacting with Distant Objects in Augmented Reality. Proceedings of the 2018 IEEE Virtual Reality Conference (IEEE VR), 2018.