Note: All times are in Nottingham local time (GMT+1)
Time
Topic
9:00-10:30
EuroVA Session 1: Opening, Keynote, Best Paper Presentation
Chair: Anna Vilanova& Daniel Archambault
Opening
Anna Vilanova& Daniel Archambault
Keynote:
“VA, ML, and Workflow Optimization”
Min Chen, Professor of Scientific Visualization at Oxford University
10:00 -10:30
Best Paper Award
Believing is Seeing: Cognitive Risks of High Confidence AI Predictions in Human Decision Making
Aarav Gupta and Alark Joshi
Honorable Mention
Going Beyond Model-Centric XAI towards Human-Centric Explanations
Bahavathy Kathirgamanathan, Gennady Andrienko, and Natalia Andrienko
10:30-11:00
Coffee Break
11:00-12:30
Session 2: Paper Presentations on Human-in-the-Loop and Reasoning in Visual Analytics
Session chair: Anna Vilanova, TU Eindhoven
Analytical Reasoning and Visualization: Estranged Bedfellows? Reclaiming a Missed Opportunity in the Human–AI Era
Aritra Dasgupta
From Data to Ficta: A Critical Reflection on Visual Analytics in the Age of Generative Models
Ignacio Perez-Messina, Silvia Miksch, and Christian Tominski
Position Paper: The Opportunity for Visual Analytics in the Age of Generative AI
S. Vink, K. Byungmoo, C. Brumar, M. Yang, K. Potter, and R. Chang
Human-in-the-Loop Visual Analytics for Validating Cybersecurity Inventories
Alessandro Palma, Matteo Miletta, Martina Valentini, and Simone Lenti
When Results Are Not Enough: Supporting Collaborative Sensemaking through Progressive Visual Analytics
A. Karagappa, P. K. Betz, M. Flatken, A. Gerndt, and B. Preim
Seeing and Understanding Process Ecosystems: A Case Study on Conference Submission.
N. Andrienko, G. Andrienko, I. Beerepoot, L. Cibulski, C. Di Ciccio, G. Meroni, C. Turkay, and T. von Landesberger
GLANCE: Strategy-Based Visual Mediation for LLM Interaction
Buchmuller Raphael
12:30 – 14:00
Lunch Break
14:00 – 15:30
Session 3: Panel “Bridging Visual Analytics and AI — looking back, moving forward”
Session chair: Cagatay Turkay
Collaborations between the visual analytics and machine learning communities goes a long way back with a couple of Dagstuhl Seminars (1, 2) over a decade ago to bring the two communities closer. Some key human-centred machine learning concepts such as human-in-the-loop approaches, interpretable models, and interactive machine learning have advanced significantly thanks to this intersection. However, these advances have not always made the impact on the wider ML and AI communities. As foundation models and generative AI reshape the field, this panel reflects on the mutual influence of visual analytics and machine learning, the tensions and gaps that remain, and the future role of human-centred approaches in AI research and practice.