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