Call for Contributions

Topological methods provide a powerful framework for reasoning about shape, structure, and patterns in complex data. They have become increasingly influential across visualization, machine learning, computational geometry, scientific computing, and data science, with applications ranging from scientific visualization and visual analytics to topology-aware learning, representation analysis, uncertainty characterization, and interpretable AI.

At the same time, research on topological methods often progresses in parallel across different communities. Visualization researchers have developed robust techniques for interactive, visual, and human-centered analysis of complex structures, while the artificial intelligence and machine learning communities have increasingly adopted topology to study representations, model behavior, robustness, generalization, and structure-aware learning. Despite these shared interests, sustained exchange between these communities remains limited.

This year’s edition of TopoInVis (TopoInVis Connect 2026) aims to address this gap by creating a focused forum for cross-community dialogue around topology. The 2026 edition inaugurates a new workshop model designed to connect the visualization community with external research areas where topological thinking plays a growing role.

The workshop welcomes contributions from researchers in visualization, machine learning, artificial intelligence, computational geometry, scientific computing, and related areas. We especially encourage submissions that expose open problems, bridge methodological perspectives, or identify opportunities for collaboration between VIS and AI communities.

The paper track will feature peer-reviewed, archival contributions on theory, algorithms, systems, applications, and evaluation of topological methods for visualization and AI. Accepted papers will be presented during the workshop and published as regular workshop papers.

Scope

Relevant topics include, but are not limited to:

• Topological methods for the analysis and visualization of complex data, including:
  • scalar, vector, tensor, and multi-field data;
  • graph and network data;
  • time-varying and time-series data;
  • high-dimensional point cloud data;
  • ensemble data;
  • data with noise, uncertainty, or ambiguity.
• Topological methods for artificial intelligence and machine learning, including:
  • topology-aware neural networks and learning architectures;
  • topological losses, regularization, and constraints;
  • persistent homology for representation learning;
  • topology-based analysis of embeddings and latent spaces;
  • topology for model robustness, generalization, and interpretability;
  • topological methods for generative models, diffusion models, and foundation models;
  • topological approaches to graph learning and geometric deep learning.
• Visualization and visual analytics for AI using topological methods, including:
  • visual analysis of learned representations;
  • topology-based explainable AI;
  • interactive exploration of model behavior;
  • visual debugging and diagnosis of machine learning models;
  • human-centered analysis of topology-aware AI systems.
• Computational methods for topological data analysis and visualization, including:
  • scalable algorithms for large and dynamic data;
  • efficient computation of persistent homology and related summaries;
  • approximation, simplification, and uncertainty-aware computation;
  • software systems and libraries for topological analysis.
• Cross-community challenges and applications, including:
  • benchmark datasets for topology-aware AI and visualization;
  • reproducibility and evaluation of topological methods;
  • integration of topological algorithms into AI and VIS workflows;
  • applications in scientific visualization, biology, medicine, materials science, climate science, fluid dynamics, data science, and other domains.

Submission

We welcome contributions as regular papers in the IEEE VGTC format (up to 9 pages of content, plus up to 2 pages of references). Full papers should present original research contributions. Paper submissions will be peer-reviewed by an international program committee. Accepted papers will be published in the IEEE Xplore Digital Library.

The Best Paper for TopoInVis Connect 2026 will be invited to submit an extended version of their work, with at least 30% additional technical content, to IEEE Transactions on Visualization and Computer Graphics (TVCG).

Important Dates

• June 14, 2026: abstract deadline for full papers
• June 30, 2026: submission deadline for full papers
• July 31, 2026: author notification
• November 9-13, 2026: IEEE VIS

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