The Visualization and Graphics Group (VIG), led by prof. dr. Alexandru C. Telea, creates visual analysis tools and methods to help scientists and researchers better process and understand large, multi-dimensional, and relational datasets in various domains such as neuroscience, genomics, software engineering, astronomy, and medicine.

Our mission is to empower experts, as well as non-data scientists, to solve Big Data problems through Visualizations, Interaction Concepts, Data Mining techniques, and Realistic Rendering methods.

VIG is part of the Department of Information and Computing Science, Utrecht University.

Research

• visualization for AI (dimensionality reduction, explaining machine learning)
• big data multiscale visualization (hierarchies, graphs, trails, tables)
• visualization in practice (decision making, user studies)
• information visualization and visual analytics (techniques, system design, evaluation)
• photorealistic computer graphics (techniques, evaluation)
• shape/image processing (skeletonization, segmentation, fairing, reconstruction)
• scientific visualization (fluid simulation, GPU- and PDE-based techniques)

Latest Publications (full list here)

2026

A. Machado, A. Telea (2026) APPA: A Cluster-Preserving Approximating Parametric Projection Algorithm. In Proc. GRIVAPP

2025

	R. Buchmüller, D. Collaris, L. Meng, A. Chatzimparmpas (2025) LangLasso: Interactive Cluster Descriptions Through LLM Explanation. 1st Workshop on GenAI, Agents, and the Future of Visualization (VIS Workshop)
	E. Katsanou, T. Mchedlidze, A. Symvonis, T. Tolias (2025) An Algorithm for Accurate and Simple-Looking Metaphorical Maps. In 33rd International Symposium on Graph Drawing and Network Visualization, GD 2025. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, pages 40:1-40:17
	A. Chatzimparmpas (2025) Visual Analytics for Explainable and Trustworthy Artificial Intelligence. IEEE Computer Graphics and Applications 45(2):100-111
	Y. Zhang, Y. Xu, X. Wang, A. Chatzimparmpas, X. Ban (2025) Decoupling Density Dynamics: A Neural Operator Framework for Adaptive Multi-Fluid Interactions. Computer Animation and Virtual Worlds 36(3):e70027

Teaching

We teach (and taught) many courses spanning all areas of visualization and graphics and for different audiences (BSc/MSc/PhD students, professionals). Details here.

Students

We have supervised many students at all levels (BSc/MSc/PhD). Selected key results here.

Research valorization

Our work has led to two innovative start-ups

• SolidSource (2006-2012): Tools and services for the visual analysis of big software data for industrial software maintenance
• GraphPolaris (2024-present): Tools and services for analyzing and visualizing large relational data