Torben Bach Pedersen is a Professor in the Data Engineering, Science, and Systems Group at the Department of Computer Science, Aalborg University. His research interests include predictive, prescriptive, and extreme-scale data analytics with digital energy as the main application area. He is an ACM Distinguished Scientist, an IEEE Computer Society Distinguished Contributor, an AAIA and AIIA Fellow, a Member of the Danish Academy of Technical Sciences, and received an Honorary Doctorate from TU Dresden for his contributions to data analytics and digital energy.

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Elisa Bertino is the Samuel D. Conte Professor of Computer Science at Purdue University, which she joined in 2004, and a research director at CERIAS. Her work spans information security and database systems, combining theory and practice across domains from medicine to the humanities, with research on access control models, secure data dissemination and provenance, trust negotiation and privacy, and data mining and security. She has served on the editorial boards of journals including ACM Transactions on Information and System Security, and chaired ACM SIGSAC. A Fellow of IEEE, ACM, and AAAS, she received the ACM Athena Lecturer Award, the IFIP Kristian Beckman Award, an honorary doctorate from Aalborg University, and the IEEE Computer Society Technical Achievement Award.

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Peter Boncz holds appointments as tenured researcher at CWI and professor at VU University Amsterdam. His academic background is in database systems, with the open-source column-store MonetDB the outcome of his PhD. He has a track record in bridging the gap between academia and commercial application, founding multiple startups. In 2008 he co-founded Vectorwise around the analytical database system by the same name, which pioneered vectorized query execution, and lightweight data compression; now broadly adopted in analytical database systems. Recent work to make data (de)compression data-parallel and AI/GPU-friendly led to the FastLanes data format. In recent years he has collaborated closely with both Databricks and with MotherDuck — a startup that is connecting DuckDB to the cloud. DuckDB originates from the Database Architectures research group, which he leads at CWI.

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Johannes Gehrke is a Technical Fellow for AI at Uber in Seattle, where he works on bringing AI to a global platform spanning mobility, delivery, and autonomy. He joined in 2026 after thirteen years at Microsoft, most recently as Technical Fellow for AI Infrastructure in CoreAI, building the inference and post-training infrastructure behind Microsoft's models. Earlier at Microsoft he led AI work across Microsoft 365 and Azure, served as Lab Director of Microsoft Research Redmond, and was Chief Architect and Head of Machine Learning for the Microsoft Teams backend. Before that he spent sixteen years on the computer science faculty at Cornell University, researching database systems, distributed systems, data science, and data privacy.

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Thomas Neumann holds the Chair of Database Systems (Lehrstuhl für Datenbanksysteme) at the Technical University of Munich. His research centers on query optimization, the computation of efficient query strategies, and query processing, their efficient execution. He studied business informatics at the University of Mannheim and received his doctorate in computer science there in 2005. Before joining TUM in 2010, he was a senior researcher at the Max Planck Institute for Informatics in Saarbrücken and completed his habilitation at Saarland University. His work includes the RDF-3X engine for scalable RDF data management, the HyPer hybrid OLTP and OLAP main-memory database system, and foundational results on compiling query plans for modern hardware. He received the Gottfried Wilhelm Leibniz Prize in 2020.

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Ismail Oukid is a principal engineer in Snowflake's Berlin office, leading the workload optimization and interactive analytics spaces. His interests span storage optimization, caching, indexing, materialization, Iceberg, and emerging analytics trends. Prior to joining Snowflake in 2019, he spent six years at SAP in Walldorf as researcher, working on leveraging modern hardware technologies in data management systems. He holds a PhD in database systems from TU Dresden.

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Ippokratis Pandis is a Distinguished Engineer at Databricks in the San Francisco Bay Area, where he works on AI-native data management systems. Previously, he spent over a decade at Amazon Web Services, where he served as Vice President/Distinguished Engineer of AWS Analytics and led the development of Amazon Redshift. Earlier, he contributed to Cloudera Impala and IBM DB2 BLU. He earned his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, where his research focused on database systems and multicore transaction processing, and a five-year Diploma in Computer Engineering and Informatics from the University of Patras, Greece. He has authored numerous research papers and has served the international database community, including as General Chair of SIGMOD 2023 and President of HPTS.

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Danica Porobic is a Consulting Member of Technical Staff at Oracle in Zurich, where she leads a small team building AI infrastructure for efficient data access across heterogeneous data sources. Her work sits at the intersection of databases, distributed systems, and AI, with a focus on making enterprise data readily available to AI applications. Since joining Oracle in 2016, she has worked on high-performance data processing and Database In-Memory. Danica holds a PhD from EPFL, where she worked with Anastasia Ailamaki on scaling transaction processing systems on non-uniform hardware platforms. Earlier in her career, she worked at Oracle Labs on Project RAPID and at Microsoft on the SQL Spatial Library.

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