EDBT/ICDT 2024 Invited Talks
We are very pleased to announce the EDBT/ICDT 2024 invited speakers.
"Adaptive database systems: Navigating chaos in a Heterogeneous
Landscape"
Ecole Polytechnique Federale de Lausanne
Abstract: Database systems face new hurdles with the rise of diverse
hardware setups and varying workloads. Evolving hardware, with its mix of different components and changing configurations, complicates how data
moves within systems, affecting performance. At the same time, the surge in data-centric analytics and powerful AI
models brings a wide range of workloads that systems must handle. The old way of designing systems based on predicted performance doesn't work well
anymore, leading to inefficiencies. This talk dives into the complexities of modern hardware and diverse workloads in database systems. It highlights the limitations of standard
approaches. It stresses the need for systems that can adapt without relying on fixed assumptions about hardware or workloads. Our goal is to
create resilient and efficient database systems ready for heterogeneity and real-time evolution.
Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL), a visiting researcher at Google, and the co-founder and Chair of the Board of Directors of RAW Labs SA, a Swiss company developing systems to analyze heterogeneous big data from multiple sources efficiently. She earned a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She has received the 2019 ACM SIGMOD Edgar F. Codd Innovations Award and the 2020 VLDB Women in Database Research Award. She is also the recipient of an ERC Consolidator Award (2013), the Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), an NSF CAREER award (2002), twelve best-paper awards in international scientific conferences. She has received the 2018 Nemitsas Prize in Computer Science by the President of Cyprus and the 2021 ARGO Innovation Award by the President of the Hellenic Republic. She is an ACM fellow, an IEEE fellow, a member of the Academia Europaea, and an elected member of the Swiss, the Belgian, the Greek, and the Cypriot National Research Councils.
"A Database Researcher Perspective on Ethical AI for Education"
Laboratoire d’Informatique de Grenoble
Abstract: The last few years has seen a proliferation of digital work and education platforms from crowdsourcing and freelancing marketplaces to MOOC-type platforms.
Educational technologies coupled with AI hold the promise of helping learners better upskill but like in any other societally critical AI-powered system, the question of ethics arises.
I will describe my perspective on rethinking the design of AIED by combining the power of data management and machine learning to better implement education theories and provide safe data-informed personalized upskilling.
Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble.
She works on exploratory data analysis and algorithmic upskilling. Sihem worked as Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and
as the coordinator of the Diversity, Equity and Inclusion initiative for the database community. Sihem Amer-Yahia is the recipient of the 2024 IEEE TCDE Impact Award For contributions to laying the interdisciplinary data engineering foundations of social computing and crowdsourcing.
"Diversity, Equity and Inclusion in Data and AI"
Michigan Institute for Data Science
Abstract: DEI (Diversity, Equity, Inclusion) has become a hot button issue today. For the past few years, there have been numerous efforts to raise awareness and to do better in hiring faculty, in training students and in running conferences.
There has been enough success, at least in some places, that the blow back has started. In this talk, I will discuss DEI in the context of data science and AI, and draw parallels with DEI in broader society. Specifically, I will talk about diversity in data types, equity for data subjects, and inclusion of data users as DEI for data.
H. V. Jagadish is Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor,
and Director of the Michigan Institute for Data Science. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ. Professor Jagadish is well known for his broad-ranging
research on information management, and has over 200 major papers and 38 patents, with an H-index of 101. He is a fellow of the ACM, "The First Society in Computing," (since 2003) and of AAAS (since 2018).
He currently chairs the board of the Academic Data Science Alliance and previously served on the board of the Computing Research Association (2009-2018). He has been an Associate Editor
for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase)
foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Since 2016, he is Editor of the Springer
(previously Morgan & Claypool) Synthesis Lecture Series on Data Management. Among his many awards, he won the David E Liddle Research Excellence Award (at the University of Michigan) in 2008,
the ACM SIGMOD Contributions Award in 2013, and the Distinguished Faculty Achievement Award (at the University of Michigan) in 2019. His popular MOOC on Data Science Ethics is available on both EdX and Coursera.
"Natural Language Data Interfaces: A Data Access Odyssey"
Athena Research Center
Abstract: Back in 1970's, E. F. Codd worked on a prototype of a natural language question and answer application that would sit on top of a relational database system.
Soon, natural language interfaces for databases (NLIDBs) became the holy grail for the database community.
Different approaches have been proposed from the database, machine learning and NLP communities. Interest in the topic has had its peaks and valleys.
After a long and adventurous journey of almost 50 years, there is a rekindled interest in NLIDBs in recent years, fueled by the need for democratizing data access and by the recent advances in deep learning and natural language processing, in particular.
Neural NLIDBs are popping up like mushrooms. Are we close to finding the holy grail of data access? In this talk, we will go over the landscape of NLIDBs covering important research milestones to the latest neural-based developments in the field.
We will present the lurking challenges that we need to surpass and the exciting research opportunities that the database community is called to solve.
Georgia Koutrika is a Research Director at Athena Research Center in Greece. Before that, she worked at HP Labs, and IBM Almaden.
She was a postdoctoral researcher at Stanford University, and she earned a Ph.D. in Computer Science from the National and Kapodistrian University of Athens, in Greece.
Her research emerges at the intersection of data management and deep learning, focusing on conversational data exploration, fairness, and learnt data management.
Her work has been incorporated in commercial products, described in 19 granted patents and 26 patent applications in the US and worldwide, and published in top-tier conferences and journals.
She is a member of the VLDB Endowment Board of Trustees, coordinating EiC for VLDB Journal, and chair of the ACM Europe Working Group on Seasonal Schools.
She is EDBT2025 Demo Chair, and she was PC co-chair for VLDB 2023, co-EiC of Proceedings of the VLDB (PVLDB) Vol 16, general chair for ACM SIGMOD 2016, and member of the PVLDB Advisory board.
She has received the EDBT 2023 Test-of-Time award, 2 best demo awards, and several industrial recognitions.
"How Database Theory Helps Teach Relational Queries in Database Education"
Duke University
Abstract: Data analytics skills have become an indispensable part of any education
that seeks to prepare its students for the modern workforce. Essential in this skill set is the ability to work with structured relational
data. Relational queries are based on logic and may be declarative in nature, posing new challenges to novices and students. Manual teaching
resources being limited and enrollment growing rapidly, automated tools that help students debug queries and explain errors are potential
game-changers in database education. We present a suite of tools built on the foundations of database theory that has been used by over 1600
students in database classes at Duke University, showcasing a high-impact application of database theory in database education.
Sudeepa Roy is an Associate Professor in Computer Science at Duke
University. She works broadly in data management, with a focus on the foundational aspects of big data analysis, which includes causal
inference and explanations for big data, debugging queries, data repair, data provenance, and probabilistic databases. Before joining Duke in
2015, she did a postdoc at the University of Washington, and obtained her Ph.D. from the University of Pennsylvania when she received a Google
Ph.D. fellowship in structured data. She co-directs the Almost Matching Exactly lab for interpretable causal inference at Duke University, is a
recipient of an NSF CAREER Award and a VLDB Early Career Research Contributions Award, and will serve as the Program Committee Chair of
the International Conference on Database Theory (ICDT) 2025.