Belgin Mutlu has a strong background in Machine Learning, Visual Data Science, Recommender Systems, and Causal Data Science. Building on this foundation, her research explores how humans interact with complex data, interpret, reason, and make decisions, and how AI-driven methods can provide meaningful, transparent support throughout this process. She works at the intersection of algorithmic modeling and human-centered design, developing machine learning–based tools, user-adaptive recommendation approaches, and methods that integrate causal reasoning to enhance interpretability and reliability. Her work reflects a broader vision: bridging data-driven automation with intuitive, trustworthy human–AI interaction.