VLDB 2025: Keynote Speakers

Alphabets, Grammars, Calculators, and the End of Hand-Crafted Systems Stratos Idreos, Gordon McKay Professor of Computer Science, Harvard John A. Paulson, School of Engineering & Applied Sciences, Harvard University Abstract: The AI revolution is transforming every scientific field and business sector, driving an unprecedented demand for data‑centric computation. As new data types, hardware platforms, and workloads appear faster than ever before, the backbone systems that power this revolution must evolve just as quickly. Yet a single system architecture—whether tuned for computing analytics, generative AI, or machine learning—faces a design space larger than 10¹⁰⁰ alternatives, and we still cling to a handful of “good” templates that each require years of manual design and implementation tuning. It is time to abandon this artisanal practice and embrace self-designing systems: systems that can reason about and refactor their own architecture. We show that by modeling the design space of systems as an alphabet of low‑level design primitives and whole architectures as sentences in a grammar over that alphabet, “systems calculators” can now synthesize fresh systems blueprints on demand. The Data Calculator explores trillions of previously unknown data‑structure variants to pick an optimal layout; Cosine and Limousine generate novel NoSQL stores that run up to three orders of magnitude faster than today’s best deployments; the Image Calculator co‑designs entirely new storage formats and neural networks to speed vision pipelines by 10×; and LegoAI and TorchTitan invent novel distributed‑training algorithms for large AI models that extract every flop and byte from modern accelerators. These results signal a future in which systems research increasingly focuses on crafting richer alphabets and grammars while machines write the sentences, freeing designers and researchers to pursue more profound questions and enabling practitioners to dial in cost, latency, and accuracy with surgical precision. Bio: Stratos Idreos is the Gordon McKay Professor of Computer Science at Harvard’s John A. Paulson School of Engineering and Applied Sciences and serves as Faculty Co-Director of the Harvard Data Science Initiative. Stratos leads DASlab, the Harvard Data Systems Laboratory. His research pursues a “grammar of data systems,” enabling machines—not humans—to design and tune systems architectures, resulting in systems that are tailored to their context, faster and more scalable. Stratos’s work has been recognized by the community with honors such as the ACM SIGMOD Jim Gray Dissertation and ERCIM Cor Baayen awards (2011), IEEE TCDE Rising Star (2015), NSF CAREER and DOE Early Career awards, the ACM SIGMOD Contributions Award (2020) and Test-of-Time Award (2022), as well as a Sloan Research Fellowship and Harvard’s McDonald Mentoring Award (2023). He has co-chaired ACM SIGMOD 2021 and IEEE ICDE 2022, co-founded the ACM/IMS Journal of Data Science, and currently serves as the chair of the ACM SoCC Steering Committee.

TBA Juliana Freire, Institute Professor at the Tandon School of Engineering and Professor of Computer Science and Engineering and Data Science, New York University Abstract: TBA Bio: Juliana Freire is an Institute Professor at the Tandon School of Engineering and Professor of Computer Science and Engineering and Data Science at New York University. She served as the elected chair of the ACM SIGMOD and as a council member of the Computing Community Consortium (CCC), and was the NYU lead investigator for the Moore-Sloan Data Science Environment, a grant awarded jointly to UW, NYU, and UC Berkeley. She develops methods and systems that enable a wide range of users to obtain trustworthy insights from data. This spans topics in large-scale data analysis and integration, visualization, machine learning, provenance management, and web information discovery, as well as different application areas, including urban analytics, predictive modeling, and computational reproducibility. She is an active member of the database and Web research communities, with over 250 technical papers (including 12 award-winning papers), several open-source systems, and 12 U.S. patents. According to Google Scholar, her h-index is 65 and her work has received over 18,000 citations. She is an ACM Fellow, a AAAS Fellow, and the recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. She was awarded the ACM SIGMOD Contributions Award in 2020. Her research has been funded by the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Gordon and Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, AT&T Research, Microsoft Research, Yahoo! and IBM. She has received M.Sc. and Ph.D. degrees in computer science from the State University of New York at Stony Brook, and a B.S. degree in computer science from the Federal University of Ceara (Brazil).

TBA Matei Zaharia, CTO and co-founder of Databricks, Associate Professor of Computer Science at UC Berkeley Abstract: TBA Bio: Matei is the CTO and co-founder of Databricks and an Associate Professor of Computer Science at UC Berkeley. He started the Apache Spark project during his Ph.D. program at UC Berkeley in 2009 and has worked on other widely used data and AI software, including MLflow, Delta Lake, and DBRX. His most recent research is about combining large language models (LLMs) with external data sources, such as search systems, and improving their efficiency and result quality. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award and the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE).