IEEE Workshop on Uncertainty Visualization: How to Make it Interpretable, Integrable, and Accessible? in conjunction with IEEE VIS 2026, Boston, USA
List of Accepted Papers Papers Invited for Presentation 1. Prediction market visualizations, betting, and uncertainty: A study of Reddit Posts and Comments Subham Sah, Alireza Karduni, Doug Markant, and Wenwen Dou 2. Similar Ratings, Different Consensus: Visualizing Agreement Uncertainty in Survey Data Vrushali Koli, Yi Meng, Eugene P. Deess, and Aritra Dasgupta 3. Distribution-Agnostic Isocontour Confidence Bounds for Robust Uncertainty Visualization of Scalar Field Data Timbwaoga A. J. Ouermi, Nina M. Gottschling, Alex Gorczowski, and Tushar M. Athawale 4. Sounds Uncertain: Exploring the Affective Aspects of Sonification for Uncertainty Visualization Marcel Dutt, Sita Vriend, Elias Elmquist, and Daniel Weiskopf 5. LoCoMapper: Local Structural Uncertainty Analysis of Mapper Graphs under Cover Perturbations Xinyuan Yan, Jixian Li, and Bei Wang 6. Uncertainty-Aware Jacobi Sets Daniel Klötzl and Daniel Weiskopf 7. Field Guide: Making Lived Uncertainty Interpretable in Women’s Return-to-Play Rhiannon Sian Owen and Jonathan C Roberts 8. Visualizing Uncertainty-to-Action Composition for Human Oversight Chisom Anyabolu, Akshat Dubey, and Georges Hattab Papers Invited for Lightning Talk and (optional) Poster Presentation 1. Fraying Certainty: Material Operations as a Design Vocabulary for Woven Textile Uncertainty Visualization Stephen Brooks 2. Seeing Through Uncertainty: Optical Operations as a Design Vocabulary for Uncertainty Visualization Stephen Brooks 3. Practice improves performance with uncertainty visualization tasks Benjamin T Files, Laura Marusich, and Mark S. Dennison Jr. 4. Visualizing Uncertainty in Non-linear Projections with Ensembles Kai Nylund, Michael Correll, and Lace M. Padilla Demos (new this year) Invited for Presentation 1. UADAPy: Uncertainty-Aware Data Analysis with Python Marina Evers, David Hägele, Daniel Klötzl, Patrick Paetzold, Nikhil Bhavikatti, Ozan Tastekin, Oliver Deussen, and Daniel Weiskopf 2. pid-depth: A Python Tool for Probabilistic Inclusion Depth Computation for Contour Ensembles Cenyang Wu, Daniel Klötzl, Qinhan Yu, Shudan Guo, Runhao Lin, Daniel Weiskopf, and Liang Zhou 3. CESET: An Interactive Tool for Uncertainty-Aware Surrogate Exploration Yuhan Duan, Xin Zhao, and Han-Wei Shen