I hold an appointment as full professor in Visual Data Analytics at the Department of Information and Computing Sciences, Utrecht University, where I lead the VIG group. Prior to this, I worked as full professor of Multiscale Visual Analytics in the Scientific Visualization and Computer Graphics (SVCG) group at the Bernoulli Institute, Faculty of Science and Engineering, University of Groningen. My research focuses on (image based) information visualization, multiscale shape processing, and visual analytics for high-dimensional and network data.

I focus on novel visual interactive techniques, algorithmic approaches, and integrated visual analytics systems to support users in navigating and exploring large amounts of relational, high-dimensional, spatial, or temporal data. A key research objective is, amongst others, to automatically assess the interestingness of visualizations and show only potentially important views from a large exploration space to reduce the users’ cognitive overload.

My main research interests include visual exploration of the inner parts and the quality of machine learning (ML) models with a specific focus on making complex ML models better understandable and explainable, as well as providing reliable trust in the ML models and their results. In addition to developing visual analytics systems, Angelos also focuses on model uncertainty quantification, utilizing deep learning architectures for visualization evaluation, and supporting users in detecting AI-generated images (deepfakes).

I specialize in Information Visualization and Human-Computer Interaction. I study decision making -how technology can help people make unbiased and informed decisions alone or in groups. I am especially interested in the kinds of decisions for which the current decision-support systems, models and people's heuristics tend to fail.

I am a Manager of Research & Strategy and a member of the Human-Centered Data Analytics research group at the Centrum Wiskunde & Informatica (CWI).
My current research includes creating linked-data driven, user-centric applications for exploring information. Currently I am investigating the use of augmented reality for exploring brain regions and related brain diseases through the Linked Brain Data repository representing neuroscience literature. At at higher level, I am interested in user-centric interaction design in the context of developing technologies. I am the director of Amsterdam Data Science (ADS), a regional ecosystem for data science researchers from academia, industry and government to share ideas, pursue research, and foster talent. I am the European Director of LIAMA, a collaboration between CWI, Inria and the Chinese Academy of Sciences, Institute of Automation.

I work on issues and methodologies around Network Visualization. I design, theoretically analyze, and evaluate algorithms for network visualization and study their practical application, experimentally and empirically, in particular in Humanities. I study network visualization algorithms with provable guarantees, e.g., that produce node-link diagrams for which quality guarantees can be formally proven. I apply general optimization techniques to solve computationally hard network visualization problems. I also work on machine learning approaches to improve network visualization algorithms. In the empirical part of my work, I study how basic principles in cognitive psychology (e.g. Gestalt rules of perception) translate to network visualization. With Philosophers and Argumentation scientists, I study how to model and visualize multidimensional opinion spaces coming from large and complex debates.

My research interests are computer graphics, material perception, realistic rendering, and virtual reality. Realistic material appearance in virtual worlds requires an enormous amount of work at every stage of the typical computer graphics workflow. A user-centric approach is required to focus efforts on how materials are perceived by the end user. I aim to develop a comprehensive model of the human visual perception of materials. This involves not just physical material characteristics such as color, gloss, texture, transparency, refraction, and translucency, but the whole appearance of the material once it's applied to a shape, illuminated, displayed, and viewed.

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PhD student

PhD Candidate working on applications of Deep Learning to Dimensionality Reduction for Data Visualization. Writes about Reinforcement Learning and Probabilistic Machine Learning in his blog.

PhD student

PhD student working on exploring the (dis-)advantages of 3D graph drawing as well as how to make the best use of Machine Learning for Graph Drawing algorithms

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PhD student

PhD student (defended 2026)

postdoc (until Nov. 2025)

PhD student (defended 2025)

PhD student (defended 2025)

lecturer (until 2024)

PhD student (defended 2024)

PhD student (defended 2024)

PhD student (defended 2023)

PhD student (defended 2022)

PhD student (defended 2021)

PhD student (defended 2020)