🔹 Affiliation: GFZ Helmholtz Centre for Geosciences, Germany

📧 Email: sips@gfz.de

Mike Sips is a researcher with extensive experience in human-centered XAI, visual analytics for data-driven decision-making, and big data concepts. He leads the Big Data and Explainable AI Research Group at the Helmholtz Centre for Geosciences, where his team focuses on advancing user-centered methodologies to make AI systems more interpretable, actionable, and aligned with diverse user needs.

His research is both theoretical and applied, collaborating with domain experts to ensure AI explainability methods are practical, ethical, and impactful. Mike has previously co-organized workshops at computer science and geoscience conferences and serves regularly on program committees for EuroVis, IEEE TVCG, VLDB Journal, and Data Mining and Knowledge Discovery.

🔹 Affiliation: Stockholm University, Sweden
📧 Email: panagiotis@dsv.su.se

Panagiotis Papapetrou is a professor of computer science at the Department of Computer and Systems Sciences at Stockholm University. His area of expertise is algorithmic data mining with a particular focus on mining and indexing sequential data, complex metric and non-metric spaces, biological sequences, time series, and sequences of temporal intervals.

He received his PhD in Computer Science at Boston University in 2009 and was a post-doctoral researcher at Aalto University from 2009 to 2013. He also served as a lecturer at the University of London between 2012 and 2013. He has participated in several EU projects, NSF grants, and Academy of Finland centers of excellence. Panagiotis has served as the general chair of the 15th International Symposium on Intelligent Data Analysis (IDA) in 2016 and 2024, has co-organized multiple workshops in data mining and knowledge discovery, and is a board member of the Swedish AI Society. He is an associate editor for the Journal of Data Mining and Knowledge Discovery and the Machine Learning Journal.

🔹 Affiliation: Humboldt University of Berlin, Germany
📧 Email: thomas.kosch@hu-berlin.de

Thomas Kosch is a professor of Human-Computer Interaction at Humboldt University of Berlin. His research focuses on leveraging AI to advance research practices and enhance transparency, reproducibility, and validity in scientific methodologies. He explores the design of explainable AI systems to foster trust and mutual understanding between humans and machines, enabling transparent and accountable collaborations.

His work includes prototyping and evaluating AI-based interfaces that prioritize user-centric design, intuitiveness, and efficiency. By studying user behavior and contextual factors, he aims to develop AI systems that are not only technically robust but also ethically and scientifically reliable.

🔹 Affiliation: GFZ Helmholtz Centre for Geosciences, Germany
📧 Email: yulia.grushetskaya@gfz.de

Yulia Grushetskaya is a researcher specializing in Explainable AI (XAI) at the Helmholtz Centre for Geosciences (GFZ). Her work focuses on exploring innovative methods for designing interactive and user-friendly explanations to bridge the gap between AI systems and end users.