30th International Conference Information Visualisation 14 International Symposium Visual Analytics and Artificial Intelligence SOAS - University of London 28-31 July 2026 Visual Analytics is the science of analytical reasoning supported by interactive visual representations. The field is closely related to Artificial Intelligence (AI), as both aim to advance knowledge discovery and decision-making through computational models and algorithmic inference. A defining characteristic of Visual Analytics, however, is its emphasis on human-centered analysis: interactive visual interfaces enable the exploration of data, the interrogation and steering of models, and the iterative refinement of hypotheses and interpretations. By coupling AI with perception-driven visual reasoning and domain expertise, Visual Analytics can increase transparency, reveal latent structure, support error detection, and strengthen confidence in analytical conclusions. With the emergence of foundation models, and in particular Large Language Models (LLMs), new methods are becoming feasible that translate unstructured natural language, documents, and LLM-generated outputs into structured representations. These representations can be explored, validated, and corrected through interactive visualizations, enabling auditable and controllable AI-assisted analysis workflows. Visual Analytics draws on computer graphics, information visualization, human-computer interaction, artificial intelligence, knowledge discovery, cognition, and visual perception.The topics of interest include but are not limited to: * Combining visual and computational methods for data analysis and Artificial Intelligence * Visual Analytics models, pipelines, and interactive approaches * Human-in-the-loop AI and interactive model steering * Visual Analytics for spatial, temporal, and spatio-temporal data, including geo-visualization * Visualization support for multi-criteria decision analysis and decision intelligence * Knowledge construction, provenance, and sensemaking in Visual Analytics systems * Guidance, recommendation, and adaptive interaction in Visual Analytics * Integrative Visual Analytics and AI systems, including hybrid symbolic-neural approaches * Medical Visual Analytics, clinical decision support, and healthcare AI workflows * AI- or Visual Analytics-enabled survey systems and methodologies. * Explainable and interpretable AI, including uncertainty-aware visualization * Visual Analytics for model debugging, validation, monitoring, and drift detection * Visual Analytics for foundation models, including Transformers and Large Language Models (LLMs) * Interaction and control techniques beyond text prompting for steering and auditing LLM-based analytics workflows * Visualization and interaction for embeddings, attention mechanisms, token-level behavior, and generative outputs * Grounded LLM-based analytics, including retrieval-augmented generation (RAG), interactive citation, and evidence tracing * Methods to translate LLM outputs into structured representations for interactive visualizations, including mixed-initiative refinement and correction * Visual Analytics for multi-document synthesis with LLMs (e.g., topic structures, argument maps, timelines, and consensus vs. disagreement views) * Visual analytic solutions for big data challenges, including scalable and distributed approaches * Visual Trend Analytics * HCI and cognitive foundations for trustworthy, accessible, and usable Visual Analytics * Visualization of algorithmic behavior and AI inference processes * Empirical performance studies, benchmarks, and reproducibility for Visual Analytics and AI systems * Evaluation methods for Visual Analytics and AI-assisted analysis * Evaluation of Visual Analytics and AI-driven systems * Collaborative Visual Analytics and AI-supported collaboration workflows * Computational steering for long-running AI-driven optimization and analysis applications * Visual Analytics for biomedical data (e.g., EHR, imaging, omics), including clinical pathway analysis * Intelligent and usable survey technologies, including adaptive questionnaires, conversational surveys, and quality-aware survey analytics * Reviews and surveys of related literature in Visual Analytics and Artificial Intelligence Prof. Dr. Kawa Nazemi Full Professor Faculty of Computer Science Head of Human-Computer Interaction & Visual Analytics Deputy Director of the Research Center for Applied Computer Science Doctoral Center for Applied Computer Science Member of Hessian.AI kawa.nazemi@h-da.de<mailto:kawa.nazemi@h-da.de> T +49-6151-533-69393 | F +49-6151-533-69413 Hochschule Darmstadt | Darmstadt University of Applied Sciences Schöfferstr. 3 64295 Darmstadt, Germany [cid:image001.jpg@01DCAE28.8A2CF800] [cid:image002.png@01DCAE28.8A2CF800]<https://vis.h-da.de/> [cid:image003.png@01DCAE28.8A2CF800]<https://www.linkedin.com/in/kawa-nazemi-069b5438/> [cid:image004.png@01DCAE28.8A2CF800]<https://twitter.com/hda_vis> [cid:image005.png@01DCAE28.8A2CF800]<https://www.youtube.com/channel/UC5LxNRfmvlgoPsGQ8LNhduA> [cid:image006.png@01DCAE28.8A2CF800]<https://www.instagram.com/hda_vis/> [cid:image007.png@01DCAE28.8A2CF800]<https://www.facebook.com/hda.vis.group/>