VLDB 2025: Call for Tutorials

VLDB 2025 invites submissions for tutorial proposals on all topics of potential interest to the conference attendees. Tutorial proposals should cover state-of-the-art research, development, and applications in specific data management related areas, and stimulate and facilitate future work. We welcome tutorials of different formats, and encourage including novel elements such as a hands-on component or a short expert panel (e.g. 30 minutes) to elicit discussion on future research related to the tutorial content. Tutorials on interdisciplinary directions, bridging scientific research and applied communities, novel and fast-growing directions, and significant applications are highly encouraged. We encourage tutorials in areas that may be different from the usual VLDB mainstream, but still very much related to VLDB mission and objectives of managing big data. We also encourage tutorials that cover topics on applying machine learning to solve data management problems and using data management systems for machine learning workloads. Tutorials should be targeted for a broad audience, and they must focus neither excessively, nor exclusively, on the authors’ own work.

Important Dates (All deadlines below are 5 PM Pacific Time.)

Submission Guidelines

Tutorial submissions must be submitted electronically, in pdf format, at the conference submission site: CMT. Submissions should be formatted using the PVLDB style templates, with a maximum length of 4 pages, inclusive of ALL material.

Proposals should include:

Accepted Tutorials

TBC

Tutorial Chairs

Hakan Ferhatosmanoglu, University of Warwick and AWS
Madelon Hulsebos, CWI

Tutorial Committee

Aditya Parameswaran, University of California, Berkeley
Andreas Kipf, UTN
Cheng Long, Nanyang Technological University
Fatma Ozcan, Google
Gerardo Vitagliano, MIT CSAIL
Jianguo Wang, Purdue University
Matteo Interlandi, Microsoft
Matthias Boehm, TU Berlin
Sharad Mehrotra, UC Irvine
Utku Sirin, Harvard University
Wenjie Zhang, University of New South Wales
Xiaofang Zhou, HKUST
Yang Cao, University of Edinburgh
Yunyao Li, Adobe
Zoi Kaoudi, IT University of Copenhagen