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.

Close

Horst Simon - Director, ADIA Lab, Abu Dhabi

---

Topic: ADIA Lab and Three Types of Climate Data Networks

Short Bio:

Dr. Horst D. Simon is an internationally recognized expert in high-performance computing and computational science, with over four decades of experience in parallel algorithms and large-scale numerical methods. After completing his Ph.D. in Mathematics at UC Berkeley, he held leadership roles across academia (Stony Brook, UC Berkeley), industry (Boeing, SGI), and national research labs (NASA Ames, Lawrence Berkeley Lab. At Berkeley Lab, he directed NERSC and served as Deputy Lab Director, earning two prestigious Gordon Bell Prizes and co-editing the biannual TOP500 list of supercomputers. Since 2023, Dr. Simon has been the founding Director of ADIA Lab in Abu Dhabi, spearheading cutting-edge research in computational and data science. He continues to advance scalable algorithms that address complex scientific and societal challenges.

Abstract

This presentation begins with a brief introduction to ADIA Lab, an independent research institute based in Abu Dhabi. ADIA Lab's mission is to advance fundamental and applied research in computational and data science, with a focus on addressing complex real-world challenges across domains such as climate, finance, and health. By fostering interdisciplinary collaborations and developing scalable algorithms and models, the lab seeks to drive innovation at the intersection of theory and practice. As an example of the projects underway at ADIA Lab, we then present our research on climate networks, structured around three complementary paradigms: (a) networks of data, where connections between geographic nodes are derived from statistical relationships, commonly referred to as Tsonis networks; (b) climate data over networks, where climate variables are defined on fixed topologies such as river basins or atmospheric grids—termed geophysical networks; and (c) networks for data, which leverages machine learning and statistical models grounded in network theory to analyze and interpret climate information. Special emphasis is placed on the first two frameworks. We examine how these network types are constructed, the insights they offer for understanding climate variability and model output, and their implications for climate governance. Finally, we discuss how integrating these perspectives can inform more robust analytical tools and policy strategies in the face of climate change.

Close

Emilio Porcu - Professor at Khalifa University, Abu Dhabi

---

Topic: ADIA Lab and Three Types of Climate Data Networks

Short Bio:

Professor Emilio Porcu is a distinguished expert in spatial-temporal statistics and data science. He earned his Ph.D. in statistics in 2005 and became a full professor by 2012 while holding Chair positions at Newcastle University and Trinity College Dublin. Since August 2020, he has served as Professor of Statistics & Data Science at Khalifa University in Abu Dhabi, and he continues as an adjunct professor at Trinity College Dublin. Dr. Porcu leads a research group specializing in space-time covariance modeling, producing nearly 180 papers and advancing theory and application in areas such as Gaussian random fields on non-Euclidean domains, covariance functions on spheres and networks, and scalable kernel-based methods. He is also a Research Fellow at ADIA Lab, contributing to innovative data science initiatives in climate, complex systems, and computational statistics.

Abstract

This presentation begins with a brief introduction to ADIA Lab, an independent research institute based in Abu Dhabi. ADIA Lab's mission is to advance fundamental and applied research in computational and data science, with a focus on addressing complex real-world challenges across domains such as climate, finance, and health. By fostering interdisciplinary collaborations and developing scalable algorithms and models, the lab seeks to drive innovation at the intersection of theory and practice. As an example of the projects underway at ADIA Lab, we then present our research on climate networks, structured around three complementary paradigms: (a) networks of data, where connections between geographic nodes are derived from statistical relationships, commonly referred to as Tsonis networks; (b) climate data over networks, where climate variables are defined on fixed topologies such as river basins or atmospheric grids—termed geophysical networks; and (c) networks for data, which leverages machine learning and statistical models grounded in network theory to analyze and interpret climate information. Special emphasis is placed on the first two frameworks. We examine how these network types are constructed, the insights they offer for understanding climate variability and model output, and their implications for climate governance. Finally, we discuss how integrating these perspectives can inform more robust analytical tools and policy strategies in the face of climate change.

Close