The UNC 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. Further, most visualization designers lack sophisticated color science knowledge required to choose the colors they really want to communicate their data. We leverage sampling-based models to better understand and predict how color manifests in different types of visualizations. We embody these models in design support tools to help users more effectively and efficiently generate custom color encodings.

Funding: National Science Foundation

Example Publications:
M.H. Hong, Z. Sundberg, & D. A. Szafir. Cieran: Designing Sequential Colormaps via In- Situ Active Preference Learning. ACM CHI 2024.
C. Tseng, A. Z. Wang, G. J. Quadri, & D. A. Szafir. "Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes." EG/VGTC EuroVis 2024.
C. Ware, D. A. Szafir, & M. Stone. Rainbow Colormaps are Not All Bad." IEEE Computer Graphics & Applications, 43(3): 88-93, 2023.
C. Tseng, G. J. Quadri, A. Z. Wang, & D. A. Szafir
. "Measuring Categorical Perception in Color-Coded Scatterplots." ACM CHI 2023.
C. Zimnicki, C. Tseng, D. A. Szafir, & K. Schloss. "Effects of data distribution and granularity on color semantics for colormap data visualizations." IEEE VIS 2023.

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.

Funding: National Science Foundation, U.S. Air Force

Example Publications:
I. Thomas, S.Y. Oh, & D.A. Szafir. "Assessing User Trust in Active Learning Systems: Insights from Query Policy and Uncertainty Visualization." ACM IUI, 2024.
E. Kravitz, H. Ray, N. Conlon, N, Ahmed, I. Thomas, & D. A. Szafir. "Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization." AIAA SciTech Forum, 2024.
H. Ray, N. Conlon, E. Kravitz, N. Ahmed, I. Thomas, D. A. Szafir, T. Wilson, S. Elting, & L. Montgomery. "Intelligent Decision Support for Target Tracking Analysis and Characterization." AIAA SciTech Forum, 2024.
M.H. Hong, L. Mark, J. Feuston, J. Rupport, J. Brubaker, & D.A. Szafir. "Scholastic: Forging Human-AI Collaboration for Inductive and Interpretive Text Analysis." UIST, 2022.
D. J. Szafir & D. A. Szafir. "Connecting Human-Robot Interaction and Data Visualizations." ACM/IEEE HRI 2021.

Enhancing 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 draw conclusions about data through visualizations and how visualizations might be designed to better support these judgments. The results from these efforts have driven scalable systems in domains ranging from biology to the humanities.

Funding: National Science Foundation

Example Publications:
G.J. Quadri, Z. Wang, Z. Wang, J. Ardono, P. Rosen, & D.A. Szafir. "Do You See What I See? Eliciting High-Level Visualization Comprehension." ACM CHI 2024.
H. Jeon*, G. J. Quadri*, H. Lee, P. Rosen, D. A. Szafir, & J. Seo. "CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering." IEEE VIS 2023.
A. Warden, J. Witt, & D. A. Szafir. "Visualizing Temperature Trends: Higher Sensitivity to Trend Direction with Single-Hue Palettes." Journal of Experimental Psychology: Applied, 28(4): 717–745, 2022.
K. W. Hall, A. Kouroupis, A. Bezerianos, D. A. Szafir, & C Collins. "Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines." IEEE VIS 2021.
M. H. Hong, J. Witt, & D. A. Szafir. "The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots." IEEE VIS 2021.

Increasing Data Accessibility

Visualization aims to make data more accessible; however, visualization designs assume a common model "user" with conventional skills and abilities. Variations in physical, visual, or cognitive ability or cultural experience may introduce substantial challenges to engaging with data visualizations. We use a combination of qualitative and quantitative methods to identify challenges to data access and propose alternative design guidance to reduce the resulting barriers. We focus on cognitive disability, where variations in working memory, mathematical reasoning, and attention impact data sensemaking.

Funding: Department of Health & Human Serives, National Institutes of Health

Example Publications:
B. Wimer, L. South, K. Wu, D.A. Szafir, M. Borkin & R. Metoyer. "Beyond Vision Impairments: Redefining the Scope of Accessible Data." IEEE TVCG, 2024.
K. Wu, M. Tran, E. Petersen, V. Kourshik, & D.A. Szafir. "Data, Data, Everywhere: Uncovering Everyday Data Experience for People with Intellectual and Developmental Disabilities." ACM CHI, 2023.
K. Wu, E. Petersen, T. Ahmad, D. Burlinson, E. S. Tanis, & D. A. Szafir. "Understanding Data Accessibility for People with Intellectual and Developmental Disabilities." ACM CHI 2021.
W. C. Payne, Y. Bergner, M. E. West, C. Charp, R. B. Shapiro, D. A. Szafir, E. Taylor, & K. DesPortes. "danceON: Culturally Responsive Creative Computing for Data Literacy." ACM CHI 2021.
K. Marriott, B. Lee, M. Butler, E. Cutrell, K. Ellis, C. Gonchu, M. Hearst, K. McCoy, & D. A. Szafir. "Inclusive Data Visualization for People with Disabilities: A Call to Action." ACM Interactions, 28(3): 47-51, 2021.

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, with a special emphasis on immersive analytics (e.g., visualizations in augmented reality) and data physicalization (e.g., visualizations using physical media). 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.

Funding: National Science Foundation, NREL, National Institutes of Health, University of Colorado Innovative Seed Program

Example Publications:
J. Kandel, C.J. Parker, Q. Zheng, H. Jiang, A. Angelopoulos, A. Neall, P. Wagh, D. Szafir, H. Fuchs, M. Lewek, & D.A. Szafir. "PD-Insighter: A Visual Analytics System to Monitor Daily Actions for Parkinson's Disease Treatment." ACM CHI, 2024.
S. Bae, T. Fujiwara, A. Ynnerman, E. Yi-Luen Do, M. Rivera, & D.A. Szafir. "A Computational Design Process for Sensing Network Physicalizations." IEEE VIS, 2023.
S. Bae, R. Vanukuru, R. Yang, P. Gyory, E. Y. Do, & D. A. Szafir. "Cultivating Visualization Literacy for Children through Curiosity and Play." IEEE VIS 2022.
S. Bae, C. Zhang, M. E. West, E. Y. Do, S. Huron & D. A. Szafir. "Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization." ACM CHI 2022.
S. Bae, D. A. Szafir, & E. Y. Do. "Exploring the Benefits and Challenges of Data Physicalization." ETIS 2022.