ITADATA2025 is honoured to announce our outstanding keynote speakers.
Angela Bonifati
Distinguished Professor at Lyon 1 University
Angela Bonifati - Distinguished Professor at Lyon 1 University
Topic: Property Graphs to the Rescue for Declarative Causal Analysis
Short Bio:
Angela Bonifati is a Distinguished Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo in Canada from 2020 and a Senior member of the French University Institute (IUF) from 2023. Her current research interests are on several aspects of data management, including graph databases, knowledge graphs, data integration and their applications to data science and artificial intelligence. She has co-authored more than 200 publications in top venues of the data management field, including five Best Paper Awards, two books and an invited paper in ACM Sigmod Record 2018. She is a recipient of an ERC Advanced Grant 2024 dedicated to leading researchers in Europe. She is the youngest recipient of the prestigious IEEE TCDE Impact Award 2023 and a co-recipient of an ACM Research Highlights Award 2023. She is the General Chair of VLDB 2026 and was the Program Chair of IEEE ICDE 2025, ACM Sigmod 2022 and EDBT 2020. She is currently an Associate Editor for the Proceedings of VLDB Vol. 19 and for IEEE TKDE and ACM TODS. She is the Chair of the ACM Sigmod Executive Committee (2025-2029) and was the President of the EDBT Executive Board and Association (2020-2024). She is a member of the IEEE Technical Committee on Data Engineering (2024-2029) and a member of the PVLDB Board of Trustees (2024-2029).
Abstract
Graphs are powerful abstractions for modeling relationships across data and enabling complex data science tasks. In this talk, I will highlight powerful declarative graph operations and delineate their counterparts in causal inference, to support data-driven, personalized decision-making across several scientific domains. I will present our work to align causal analysis with property graphs—the foundation of modern graph databases—by rethinking graph models to incorporate hypernodes, structural equations, and causality-aware query semantics. By unifying graph databases with causal reasoning, causal tasks such as interventions and counterfactuals are mapped to property graph manipulation and transformation, combining expressiveness with computational efficiency.
