Alexandra Meliou

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About

Alexandra Meliou is a leading researcher in data management, database theory, and algorithmic fairness, with a focus on understanding and mitigating bias in data-driven systems. Her work bridges the gap between theoretical foundations and practical tools for responsible data analysis. She is best known for her contributions to causality in databases, developing methods to explain query results and identify root causes of data errors. Her influential research on "nonmyopic informative path planning in spatio-temporal models" (80 citations) pioneered efficient sensing strategies for robots and sensor networks, demonstrating her early impact in adaptive data collection. Meliou has also made seminal contributions to data provenance, fairness in ranking and classification, and interpretable machine learning. Her work on causal reasoning for data debugging and explanation has been widely adopted, earning her a prestigious NSF CAREER Award and multiple best paper honors. With over 5,000 total citations, Meliou’s research is essential reading for anyone interested in building transparent, equitable, and trustworthy data systems.