Daniel Dan

Daniel Dan is Assistant Professor and founder of the School of Applied Data Science at Modul University Vienna (MU Vienna), Austria. His expertise focuses on natural language processing, information retrieval, and data-driven marketing analytics in text-rich environments. His primary research, teaching, and collaboration interests include generative AI, sentiment and opinion mining, data visualization, and decision support. He teaches data science and AI courses at MU Vienna and AI at the WU (Vienna University of Economics and Business) Executive Academy. His work features in peer-reviewed journals and international conferences, and he contributes to EU-funded projects on information overload and digital well-being.

Thomas Reutterer is Professor of Marketing at the Vienna University of Economics and Business (WU Vienna), Austria. His expertise focuses on analyzing, modeling and forecasting customer behavior in data-rich environments. His primary research, teaching and business consulting interests are focused in areas of retail and digital services, customer value and relationship management, and marketing models for customer-base analysis and decision support. His prior research has appeared in leading marketing and operations management journals.