EDBT/ICDT 2025 Invited Keynotes

We are very pleased to announce the EDBT/ICDT 2025 invited speakers. Titles, abstracts and more speakers will be announced soon.

"Data Management Perspectives on Prescriptive Analytics"

by Alexandra Meliou

University of Massachusetts Amherst

Abstract: Decision makers in a broad range of domains, such as finance, transportation, manufacturing, and healthcare, often need to derive optimal decisions given a set of constraints and objectives. Traditional solutions to such constrained optimization problems are typically application-specific, complex, and do not generalize. Further, the usual workflow requires slow, cumbersome, and error-prone data movement between a database, and predictive-modeling and optimization packages. All of these problems are exacerbated by the unprecedented size of modern data-intensive optimization problems. The emerging research area of in-database prescriptive analytics aims to provide seamless domain-independent, declarative, and scalable approaches powered by the system where the data typically resides: the database. Integrating optimization with database technology opens up prescriptive analytics to a much broader community, amplifying its benefits. We will discuss how deep integration between the DBMS, predictive models, and optimization software creates opportunities for rich prescriptive-query functionality with good scalability and performance. Summarizing some of our main results and ongoing work in this area, we will highlight challenges related to usability, scalability, data uncertainty, and dynamic environments, and will argue that perspectives from data management research can drive novel strategies and solutions.

Alexandra Meliou is a Professor at the College of Information and Computer Sciences at the University of Massachusetts Amherst. Her research focuses on problems related to the use and understandability of data and data-driven systems, with contributions in the areas of causality, explanations, data quality, fairness, and prescriptive analytics. Prior to joining UMass Amherst, she was a postdoctoral researcher at the University of Washington. She received her PhD and MS degrees from the University of California Berkeley. She is currently a member of the VLDB Endowment Board of Trustees, member of the PVLDB Advisory Board, co-chair of the Joint DB Task Force on Reviewing Processes, and served as a PC co-chair for SIGMOD 2024. She has received recognitions for research, teaching, and service, including a CACM Research Highlight, an ACM SIGMOD Research Highlight Award, an ACM SIGSOFT Distinguished Paper Award, an NSF CAREER Award, a Google Faculty Research Award, multiple Distinguished Reviewer Awards, and a Lilly Fellowship for Teaching Excellence.

"How to make “LLM" mean Lucrative Lasting Magic?"

by Carlo Curino

Microsoft Gray Systems Lab (GSL)​

Abstract: In this talk, we present an industry vantage point on how to leverage LLMs to delight our internal and external users. We explore the three key ingredients to build “magical” experiences and present concrete (positive and negative) examples from production deployments of NL2SQL technologies, On-Call engineer support copilots, and ongoing work to build an agentic Virtual Data-Scientist. We conclude discussing some of the lessons learned and challenges in performing rigorous/deep research in hyper-fast hype-ridden space.

Carlo Curino is the Partner Director of Research leading the Microsoft Gray Systems Lab (GSL), an applied research group working at the intersection of Databases/Systems/Machine Learning as part of Azure Data. Before this Carlo was a Principal Scientist working on large-scale distributed systems, with a focus on scheduling for BigData clusters; this line of research was co-developed with several team members and open-sourced as part of Apache Hadoop/YARN. Intrinsically, this research work enables us to operate the largest YARN clusters in the world (deployed on 250k + servers within Microsoft). Prior to joining Microsoft he was a Research Scientist at Yahoo!, primarily working entity deduplication and scale and mobile+cloud platforms. Carlo spent two years as a Post Doc Associate at CSAIL MIT working with Prof. Samuel Madden and Prof. Hari Balakrishnan, working on relational databases in the cloud. At MIT he also served as the primary lecturer for the course on databases CS630, taught in collaboration with Mike Stonebraker. Carlo received a Bachelor in Computer Science at Politecnico di Milano. He participated in a joint project between University of Illinois at Chicago (UIC) and Politecnico di Milano, obtaining a Master Degree in Computer Science at UIC and the Laurea Specialistica (cum laude) in Politecnico di Milano. During the PhD at Politecnico di Milano advised by Letizia Tanca, Carlo spent two years as a visiting researcher at UCLA.

"Range data: where to find them and what to do with them"

by Nikos Mamoulis

University of Ioannina

Abstract: Range data are ubiquitous in single- and low-dimensional spaces, playing a crucial role in various applications. They model validity periods of object versions in temporal databases, represent uncertainty or confidence intervals in probabilistic databases, and serve as object approximations in spatial databases, among other uses. In this talk, I will explore the diverse characteristics of range data — such as extent, overlap, and dynamics — across different applications and discuss their handling requirements case by case. I will then provide an overview of range data management, covering techniques from computational geometry, spatial and temporal data processing, and modern in-memory approaches. Finally, I will discuss emerging trends in applying machine learning to indexing and query optimization for range data, highlighting key challenges and future research directions.

Nikos Mamoulis is a professor at the Department of Computer Science and Engineering, University of Ioannina (UoI), and a lead researcher at Archimedes Research Unit of Athena RC. Before joining UoI, he was a faculty member at the Department of Computer Science, University of Hong Kong. He holds a BEng degree in computer engineering and informatics from the University of Patras, Greece, and a PhD in computer science in from HKUST. His research focuses on the management and mining of complex data types. His work on spatio-temporal data management has received best paper and test-of-time awards. He is the recipient of a Marie Curie fellowship (2014-2018) and he has been granted over 15 research projects as a PI in Hong Kong and Greece. He is a senior ACM member. He has participated in many organization boards of international conferences and has been a PC member in more than 120 program committees of top conferences in database research (e.g., SIGMOD, VLDB, ICDE, EDBT, KDD). He has been an associate editor and an editorial board member in several journals including TKDE, VLDBJ, KAIS, Geoinformatica, and ACM TSAS.

"The Quest for Faster Join Algorithms"

by Paris Koutris

University of Wisconsin-Madison

Abstract: Joins are the cornerstone of relational databases. Surprisingly, even after several decades of research in the systems and theory database community, we still lack an understanding of how to design the fastest possible join algorithm. In this talk, we will present the exciting progress the database theory community has achieved in join algorithms over the last two decades. The talk will revolve around five key ideas fundamentally shaping this research area: tree decompositions, data partitioning, leveraging statistical information, enumeration, and algebraic techniques.

Paris Koutris is an associate professor in Computer Sciences at the University of Wisconsin-Madison. His research lies in the intersection of data management theory and practice, focusing on data processing for massively parallel systems, data markets, efficient query processing, and managing uncertain data. He has won the 2016 SIGMOD Jim Gray Dissertation Award for his work on the foundations of parallel data processing, as well as the 2023 PODS Alberto O. Mendelzon Test-of-Time award.