Call for Contributions

Uncertainty visualization has been recognized as one of the grand research challenges that must be addressed for trusted scientific discovery and decision-making. Visualization and analysis of uncertainty is however nontrivial given many difficult challenges, including computation, rendering, perception, and cognition, and decision-making under uncertainty. This workshop therefore aims to set up a platform that would promote interdisciplinary interaction and dissemination of work to advance uncertainty visualization research. This workshop will be the important step to enhancing theoretical and practical understanding of analysis and treatment of uncertainty across diverse domains through exciting submissions and interaction among experts within and outside the visualization field.

The workshop welcomes researchers with expertise in diverse areas, including but not limited to computation, visualization, perception, cognition, application science, mathematics, applied math, machine learning, to submit short/full papers or posters on effective analysis and decision-making under uncertainty. This call is flexible to cover a broad range of studies in uncertainty analysis, including novel research contributions, domain-specific or general requirements for successful uncertainty analysis, obstacles to understanding data uncertainty presented through use cases, and successful uncertainty visualization workflows for robust solutions.

The workshop welcomes submissions of short-length papers, full-length papers, and posters. The accepted papers will be presented during the workshop, and the accepted posters will be presented at the main poster event at VIS and give a lightning talk during the workshop.

Scope

Relevant topics include (but are not limited to):

• Applications: Domains-specific use cases for uncertainty visualization and analysis of scientific or information data with domains including but not limited to climate science, energy science, material science, quantum computing, and machine learning.
• Techniques: Techniques to quantify and convey uncertainty in 2D/3D/high-dimensional (scientific or information) data. These cover a broad range of concepts, including but not limited to statistics, machine learning, computing, rendering, perception, and cognition that are fundamental to uncertainty
• Software and tools: Uncertainty visualization software or tools to convey the uncertainty in general or application-specific scientific or information data.
• Decision frameworks: Methods, workflows, and application use cases for robust decision-making under uncertainty.

Important Dates

• Deadline for submission: June 26, 2024 July 3, 2024
• Author notification: July 31, 2024
• Camera-ready papers due: August 18, 2024
• IEEE VIS: October 13-18

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