Program

Keynote

Dr. Bei Wang Phillips, Associate Professor, University of Utah

Title: Visualizing Uncertainty in Topological Descriptors: From Robust Features to Trustworthy Discovery

Abstract: Topological descriptors, including persistence diagrams, merge and contour trees, and Morse complexes, provide compact and expressive representations of structure in complex scientific data. However, these descriptors are shaped by uncertainty arising from measurement noise, limited sampling, ensemble variability, modeling assumptions, and analysis choices. When such uncertainty is not visible or interpretable, topology-based analysis can imply a level of confidence that the data and computational pipeline do not support. This talk explores how uncertainty visualization can connect mathematically grounded topological descriptors with the practical reasoning needs of domain scientists. I will discuss approaches for quantifying, visualizing, and interactively exploring uncertainty in topological features, with attention to how feature significance, correspondence, and stability vary across data and parameter choices. The central premise is that uncertainty should not be treated as an error bar added after topological analysis; rather, it is a structured, multiscale property of both the data and the analytical process. By integrating topology, geometry, statistics, and interactive visualization, we can create uncertainty-aware topological representations that support comparison, explanation, and more trustworthy scientific discovery.

Bio: Dr. Bei Wang Phillips is an Associate Professor in the Kahlert School of Computing, an Adjunct Associate Professor in the Department of Mathematics, and a faculty member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She received her Ph.D. in Computer Science from Duke University. Her research lies at the intersection of topological data analysis, data visualization, and computational topology. She develops and integrates topological, geometric, statistical, data mining, and machine learning methods with visualization to enable scientific discovery from large, complex data. Her work has been supported by the NSF, NIH, and DOE. Dr. Phillips received a DOE Early Career Research Program Award in 2020, an NSF CAREER Award in 2022, and the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Biden in 2024, the highest honor bestowed by the U.S. government on early-career scientists and engineers.

Detailed Run of Show:

Opening - 1-1:05pm

Paper Presentations [Best Paper and Honorable Mention] - 1:05-1:30pm

Each full paper presentation will be 7 mins including Q&A. Each short paper presentation will be for 6 mins including Q&A.

¹[Best Paper 🏆] Subham Sah, Alireza Karduni, Doug Markant, and Wenwen Dou, "Prediction market visualizations, betting, and uncertainty: A study of Reddit Posts and Comments" [Short paper, 1:05pm-1:11pm].

²[Best Paper 🏆] Vrushali Koli, Yi Meng, Eugene P. Deess, and Aritra Dasgupta, "Similar Ratings, Different Consensus: Visualizing Agreement Uncertainty in Survey Data" [Full paper, 1:11pm-1:18pm].

³[Honorable Mention 🏅] Timbwaoga A. J. Ouermi, Nina M. Gottschling, Alex Gorczowski, and Tushar M. Athawale, "Distribution-Agnostic Isocontour Confidence Bounds for Robust Uncertainty Visualization of Scalar Field Data" [Short paper, 1:18pm-1:24pm].

⁴[Honorable Mention 🏅] Xinyuan Yan, Jixian Li, and Bei Wang, "LoCoMapper: Local Structural Uncertainty Analysis of Mapper Graphs under Cover Perturbations" [Short paper, 1:24pm-1:30pm].

Keynote - 1:30-2:00pm

Demos - Uncertainty Visualization in Action [New this year!] - 2:00-2:30 pm

Each demo will be 6-8 mins including Q&A.

¹ Marina Evers, David Hägele, Daniel Klötzl, Patrick Paetzold, Nikhil Bhavikatti, Ozan Tastekin, Oliver Deussen, and Daniel Weiskopf, "UADAPy: Uncertainty-Aware Data Analysis with Python" [2pm-2:08pm].

² Cenyang Wu, Daniel Klötzl, Qinhan Yu, Shudan Guo, Runhao Lin, Daniel Weiskopf, and Liang Zhou, "pid-depth: A Python Tool for Probabilistic Inclusion Depth Computation for Contour Ensembles" [2:08pm-2:16pm].

³ Yuhan Duan, Xin Zhao, and Han-Wei Shen, "CESET: An Interactive Tool for Uncertainty-Aware Surrogate Exploration" [2:16pm-2:24pm].

⁴ Timbwaoga A. J. Ouermi, Alex Gorczowski, David Pugmire, Chris R. Johnson, Kenneth Moreland, and Tushar M. Athawale, "Visualizing Scientific Data Uncertainty with AI-Assisted UVisBox (Python) and Parallel Viskores (C++) Libraries" [2:24pm-2:30pm].

Break - 2:30-3:00pm

Paper Presentations - 3:00-3:26 pm

Each full paper presentation will be 7 mins including Q&A. Each short paper presentation will be for 6 mins including Q&A.

¹ Marcel Dutt, Sita Vriend, Elias Elmquist, and Daniel Weiskopf, "Sounds Uncertain: Exploring the Affective Aspects of Sonification for Uncertainty Visualization" [Full paper, 3:00pm-3:07pm].

² Daniel Klötzl and Daniel Weiskopf, "Uncertainty-Aware Jacobi Sets" [Full paper, 3:07pm-3:14pm].

³ Rhiannon Sian Owen and Jonathan C Roberts, "Field Guide: Making Lived Uncertainty Interpretable in Women’s Return-to-Play" [Short paper, 3:14pm-3:20pm].

⁴ Chisom Anyabolu, Akshat Dubey, and Georges Hattab, "Visualizing Uncertainty-to-Action Composition for Human Oversight" [Short paper, 3:20pm-3:26pm].

Lightning Talks - 3:26-3:35 pm

Each lightning talk will be ~2 mins including Q&A.

¹ Stephen Brooks, "Fraying Certainty: Material Operations as a Design Vocabulary for Woven Textile Uncertainty Visualization" [Short paper, 3:26pm-3:28pm].

² Stephen Brooks, "Seeing Through Uncertainty: Optical Operations as a Design Vocabulary for Uncertainty Visualization" [Short paper, 3:28pm-3:30pm].

³ Benjamin T Files, Laura Marusich, and Mark S. Dennison Jr., "Practice improves performance with uncertainty visualization tasks" [Short paper, 3:30pm-3:32pm].

⁴ Kai Nylund, Michael Correll, and Lace M. Padilla, "Visualizing Uncertainty in Non-linear Projections with Ensembles" [Short paper, 3:32pm-3:34pm].

Breakout Sessions - 3:35-4:15pm

Breakout Session Chairs Report Back - 4:15-4:25pm

Closing - 4:25-4:30pm