Call for Contributions

The past uncertainty visualization workshops highlighted three persistent bottlenecks—interpretability, integrability, and accessibility— of uncertainty visualization that cut across domains, tools, and user groups. Specifically, although a number of new uncertainty visualization techniques have been developed over the past decade, their growing complexity and diversity make them difficult to interpret for non-experts and even experienced researchers. This interpretability gap, in turn, hinders integrability: scientists struggle to incorporate uncertainty visualization into their analysis pipelines, and computational overhead of uncertainty propagation further limits its integration into existing workflows. Finally, the lack of uncertainty - aware capabilities in commonly used tools and software ecosystems reduces accessibility and prevents broader use.

This workshop welcomes paper and demo submissions that advance interpretable representations, integrable computational methods, and accessible tools and frameworks. While alignment with these themes is encouraged, novel contributions beyond them are also welcome.

Scope

Relevant topics include (but are not limited to):

• Interpretability: Methods, designs, empirical/user studies, and theoretical frameworks that improve users’ ability to understand, compare, and reason under uncertainty, particularly in high-dimensional, ensemble-based, and AI-driven contexts.
• Integrability: Computational techniques, data representations, and system architectures that incorporate uncertainty visualization into complex workflows, AI and multi-agent pipelines, HPC environments, and decision-support systems.
• Accessibility: Tools, libraries, and pedagogical techniques that lower barriers to adoption and enhance software interoperability of uncertainty visualization, including visualization toolkits, web-based interfaces, and guided-exploration systems.
• Algorithms: Novel techniques and algorithms that enable users to model, propagate, quantify, and visualize uncertainty in univariate and multivariate scientific and information data.

See submission for details relevant to the paper and demo submission format.

Important Dates

• Deadline for submission: July 10, 2026
• Author notification: August 7, 2026
• Camera-ready papers due: August 18, 2026
• IEEE VIS: November 9-13, 2026

All deadlines are in Anywhere on Earth (AoE) time zone.