From Embeddings to Exploration: Engineering Interactive Latent Space Visualizations for AI Model Sensemaking

Our paper "From Embeddings to Exploration: Engineering Interactive Latent Space Visualizations for AI Model Sensemaking" (PDF) has been accepted at EICS 2026 and will appear in the EICS issue of Proceedings of the ACM on Human-Computer Interaction. This is work by Sebe Vanbrabant together with Jarne Thys, Gilles Eerlings, Gustavo Rovelo Ruiz, Davy Vanacken, and myself.

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BeatriXR: Comprehensive and Adaptive Feedforward Support for Guidance in Virtual Reality

Our paper "BeatriXR: Comprehensive and Adaptive Feedforward Support for Guidance in Virtual Reality" (PDF) has been accepted at EICS 2026 and will appear in the EICS issue of Proceedings of the ACM on Human-Computer Interaction. This is work by Valentino Artizzu together with Gustavo Rovelo Ruiz, Lucio Davide Spano, and myself.

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RaDIA: Visual Analytics for Systematic Sample Association in Nuclear Weapon Test Verification Workflows

Our late-breaking report "RaDIA: Visual Analytics for Systematic Sample Association in Nuclear Weapon Test Verification Workflows" (PDF) has been accepted at EICS 2026 in Patras, Greece. This is work by Stian Verherstraeten together with Christophe Gueibe (SCK CEN), Gustavo Rovelo Ruiz, and myself.

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Making It Work Is the Work: Engineering Maturity as Epistemic Work

We contribute some reflections on our attempts to progress HCIxFabrication research from the lab to the market in a short paper "Making It Work Is the Work" (to be discussed at the RealFab'26 workshop at CHI 2026 in Barcelona). This is work with my colleagues Danny Leen, Stig Konings, and Raf Ramakers at the Digital Future Lab (UHasselt – Flanders Make).

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DELEGACT: Let the Robot Watch, Then Decide Who Does What

Our extended abstract "Learning to Delegate and Act with DELEGACT: Multimodal Language Models for Task-Level Human–Cobot Planning in Industrial Assembly" has been accepted at CHI 2026 in Barcelona. This is work by Bram Verstappen together with Dries Cardinaels, Danny Leen, and Raf Ramakers at the Digital Future Lab (UHasselt - Flanders Make).

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Teaching as Training: Incremental and Iterative AI Skill Development

We presented our contribution “Teaching as Training: Iterative and Incremental AI Skill Development” () at the EURECA-PRO Education & Research Days in Hasselt, held under the theme Glocalising Universities: A Shifting Horizon. This is joint work with Jolien Notermans (Department of Educational Development, Policy and Quality Assurance) and Sarah Doumen (Faculty of Sciences) at Hasselt University. More details on the publication page. The visual story is generated using StoryBookly.

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DIVERSE: Finding the Many Faces of AI Decision-Making

Our paper “DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration” () has been accepted at ICLR 2026, one of the top venues for machine learning research. This is joint work with my PhD student Gilles Eerlings, Brent Zoomers, Jori Liesenborgs, and Gustavo Rovelo Ruiz at the Digital Future Lab. More details on the publication page.

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Every Move You Make: Helping Operators See Where Their Robot Will Go

Our paper "Every Move You Make: Visualizing Near-Future Motion Under Delay for Telerobotics" () has been accepted at CHI 2026 in Barcelona — the premier conference for human-computer interaction research. This is joint work with my PhD student Dries Cardinaels, Raf Ramakers, Tom Veuskens, Thomas Pietrzak (Univ. Lille, Inria), and Gustavo Rovelo Ruiz at the Digital Future Lab (UHasselt - Flanders Make). More details on the publication page.

→ Paper page on driescardinaels.be

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Our short paper, Challenges and Opportunities for Delay-Invariant Telerobotic Interactions, has been accepted for the 29th ACM International Conference on Intelligent User Interfaces (IUI 2025).

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In AI research, model multiplicity can help users better understand the diversity of AI predictions. Our new system “AI-Spectra” provides a visual dashboard to harness this concept effectively. Instead of relying on a single AI model, AI-Spectra uses multiple models—each seen as an expert—to produce predictions for the same task. This helps users see not only what different models agree or disagree on, but also why these differences occur. Gilles Eerlings (a FAIR PhD student ) and Sebe Vanbrabant where the main contributors for this work and combined machine learning, model multiplicity and visualisations that focus on the characteristics of an AI model, instead of explaining the behaviour.

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