Research diversity, equity, and inclusion in academia and industry
Keynote Session: Tuesday, May 5 | from 13:30-15:00
Title: Environment Sustainability in Database Systems
Speaker: Bettina Kemme (McGill University)
Abstract
Energy use and environmental impact have long been secondary considerations in database systems research. While database performance improvements often bring incidental energy benefits, the increasing integration of machine learning into DBMS components and the sheer quantity of data in current data analytics change this relationship. ML models and inference pipelines can introduce significant energy costs and the computational costs of large-scale analytics are huge, prompting a need to reassess how we evaluate and design modern data management systems.
This talk introduces the core concept of energy consumption and carbon footprint of computing, then examines how advanced database technology, including ML-based components, may alter the energy and carbon characteristics of database software. I will highlight emerging challenges and outline several research directions, including the development of meaningful energy‑impact metrics for database operations and possible mechanisms for reducing the energy footprint of DBMSs. This discussion aims to motivate a renewed focus on sustainability as a first‑class objective in the design and evaluation of future data management systems.
Bio
Bettina Kemme is a Professor of the School of Computer Science at McGill University, where she leads the Distributed Information Systems lab. Her general research interests lie in large-scale data management and analytics. Her recent projects involved graph-based database management, sustainable data systems for data science, and data analytics scheduling. Bettina holds a PhD degree in Computer Science from ETH Zurich. She has published well over 100 publications in major journals and conferences in the areas of database systems and distributed systems. She has served on the editorial board of the VLDB Journal and Information Systems and has been on the program committee or area chair of SIGMOD, VLDB, ICDE, EDBT, ICDCS, Middleware, SRDS and many more. She was the PC Co-Chair of Middleware 2017, DEBS 2023 and EDBT 2025, and is a senior IEEE member. She co-created the Workshop series on Data Systems meet Data Science (DSDS).
Title: A case for jumping off cliffs in one's career
Speaker: Kavitha Srinivas (IBM Research)
Abstract
TBA
Bio
Kavitha Srinivas is a researcher specializing in semantic data management and knowledge graphs. Her work spans areas in cognitive science (studying human memory) to applications of AI to data management. She has worked in academia as a professor of Cognitive Psychology, as a CTO of a data integration startup and as a researcher in the field of artificial intelligence at IBM Research.
Title: The Long Road to Mid-Career: Growth, Challenges, and Changing Perspectives in Academia
Speaker: Angela Bonifati (Lyon 1 University (France), and CNRS Liris research lab)
Abstract
What does it really feel like to move from early-career to mid-career responsibility? This talk reflects on the academic journey as it unfolds over time—rarely linear, often unexpected. Mid-career brings more independence in shaping research, collaborations, and mentoring. But it also comes with new challenges: balancing roles, sustaining impact, and navigating visibility. Along the way, broader community dynamics such as publication practices and representation quietly shape opportunities. By combining personal experience with observations from analysis on publication data, this talk offers a grounded view of academic growth. Ultimately, it is a story about evolving identity, resilience, and finding one’s place in a changing research landscape.
Bio
Angela Bonifati is a Distinguished Professor of Computer Science at Lyon 1 University and at the CNRS LIRIS research laboratory, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo, Canada since 2020, and a Senior Member of the French University Institute (IUF) since 2023. Her current research interests span several aspects of data management, including graph databases, knowledge graphs, and data integration, as well as their applications to data science and artificial intelligence. She has co-authored numerous publications in top venues in the data management field, including five Best Paper Awards, two books, and an invited paper in ACM SIGMOD Record (2018). She is an ACM Fellow and the recipient of a European Research Council Advanced Grant (2024). Her work has been recognized with the VLDB Women in DB Research Award (2025), the IEEE TCDE Impact Award (2023), and an ACM SIGMOD Research Highlights Award (2023). She is the General Chair of VLDB 2026 and has previously served as Program Chair of IEEE ICDE 2025, ACM SIGMOD 2022, and EDBT 2020. She is currently an Associate Editor for the Proceedings of the VLDB (Volume 19), IEEE TKDE, and ACM TODS. She is also the President of ACM SIGMOD (2025–2029), a member of the IEEE Technical Committee on Data Engineering (2024–2029), and a member of the PVLDB Board of Trustees (2024–2029).