Modern data-intensive applications rely on graph databases, yet graphs frequently contain inconsistencies that violate structural constraints. My work addresses this from a human-centric perspective: rather than fully automated fixes, I develop interactive repair frameworks that keep users in control of the process.
Designing technology for societal challenges requires more than functional requirements — it requires understanding the perspectives, experiences, and emotions of the people affected. My work in this area develops empathy-centric frameworks that bridge the gap between end-users and decision-makers.
Large-scale data management cannot rely on machines alone. Human judgment is essential for handling ambiguity, ensuring quality, and interpreting domain-specific context. My research develops human-in-the-loop frameworks that integrate crowd contributions intelligently at each stage of the data pipeline.