Philippe Bonnet

Philippe Bonnet is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen since 2023, where he is affiliated with the Programming Languages and Theory of Computation section. Previously, he served as a Professor at the IT University of Copenhagen from 2009 to 2023 and as an Associate Professor at DIKU from 2001 to 2009. His academic journey began with a PhD from Universite Savoie Grenoble, France in 1999.

Education:

• PhD from Universite Savoie Grenoble, France (1999)

Philippe Bonnet is an experimental computer scientist with expertise in database systems and storage system software. His research focuses on the intersection of database systems and storage technologies, with significant contributions to the uFlip Benchmark, Linux multiqueue block layer, Linux framework for Open-Channel SSDs, OX architecture for computational storage, xNVMe library, and Delilah prototype for eBPF offload. His work spans database tuning, sensor networks (Cougar project at Cornell, Hogthrob and Mana projects at U.Copenhagen), and edge-based AI for predictive maintenance through the DAPHNE Project. His technical contributions bridge theoretical computer science with practical systems development.

His recent publications demonstrate a strong trend toward integrating database systems with advanced storage technologies, particularly focusing on computational storage, CXL integration, and reliability mechanisms between databases and SSDs. His work bridges computer architecture, operating systems, and database systems to create more efficient data processing infrastructures, with increasing emphasis on edge-based applications and reproducibility in computer science research.

Scientific Awards:

• ACM SIGMOD Contribution Award (2020)

Professor Bonnet has served in numerous editorial and committee roles, including as Editor of Big Data Research Journal since 2021, Member of the VLDB Endowment (2020-2025), Member of ACM EIGREP, and long-standing involvement with ACM conferences. His work on the DAPHNE Project focuses on creating an open and extensible system infrastructure for integrated data analysis pipelines, particularly for edge-based AI applications in predictive maintenance. He co-authored a book on database tuning with Dennis Shasha and has been instrumental in advancing storage system software through multiple high-impact projects and collaborations.