Call for Abstracts

We invite short extended abstracts on ML for Systems and Systems for ML. This edition particularly encourages contributions that explore how recent advances in foundation models, Large Language Models (LLMs), and agentic AI are changing the interaction between machine learning and data systems, while retaining the broader scope of the workshop.

The extended-abstract format provides a forum for discussing research at an earlier stage than conventional workshop or conference papers. Submissions do not need to present completed research or a comprehensive experimental evaluation. The goal is a concrete and compelling basis for discussion, not necessarily a complete research story.

Submit an Abstract Deadline: December 8, 2026

Key Dates & Format

Workshop
March 16, 2027 at LMU Munich, co-located with BTW 2027
Submission deadline
December 8, 2026
Notification
December 18, 2026
Abstract format
Extended abstract, 2–4 pages excluding references, LNI style, plus a short author bio
Presentation
Short talk in a thematic session, with substantial time for discussion

Types of Contributions

We welcome contributions that present:

  • Emerging ideas and ongoing work: new approaches or early-stage research that could benefit from discussion and feedback;
  • Positions and perspectives: viewpoints on current developments or directions for future research;
  • Open research problems: challenges that deserve greater attention from the ML and data systems communities;
  • Lessons learned: insights from research or practice, including unexpected findings and negative results; and
  • System experiences: experiences from developing or deploying approaches at the intersection of machine learning and data systems.

Topics

Topics of interest include, but are not limited to:

ML for Systems

  • Learning for query processing and optimization
  • Learned data structures and data organization
  • Learning for data integration and data quality
  • Learning for system configuration, tuning, and resource management
  • Adaptive and autonomous data systems
  • Foundation models, LLMs, and agentic AI for data systems
  • Robustness, generalization, and evaluation of learned approaches

Systems for ML

  • Data management for machine learning and AI applications
  • Data preparation, integration, and quality for machine learning
  • Management and optimization of ML data and model pipelines
  • Query processing and data access for AI applications
  • Data management for foundation models, LLMs, and agentic AI
  • Data management for multimodal and emerging AI applications
  • Efficient and scalable data systems for machine learning

We particularly encourage contributions that connect the two directions, investigating how advances in AI are changing data systems as well as how data-management research can support emerging AI applications.

Submission

The requirements for the submission are as follows:

  • an extended abstract of 2–4 pages (excluding references) on an exciting research or industry problem, and
  • a short summary about the author (short bio), including corresponding work related to the problem. Links to the author’s homepage and/or LinkedIn can also be included.

Please use the submission instructions of the main conference, LNI style (see https://gi.de/service/publikationen/lni), for the abstract.

Additionally, you can submit a 2-minute video about your contribution, e.g., a demonstration or a presentation about your work. Instead of a video, a maximum of 3 slides explaining the idea can also be submitted.

Where to submit: Submission Form

Selection & Proceedings

Submissions will be selected based on their relevance to the workshop, the novelty of the idea or perspective, and their potential to stimulate discussion among workshop participants. Selected abstracts will be grouped into thematic sessions based on common themes emerging from the submissions.

Presenters are encouraged to use their talks not only to introduce their work, but also to highlight aspects that would benefit from feedback and discussion.

Extended abstracts are intended to support presentation and discussion at the workshop, so authors can subsequently develop their work further for publication at archival venues. As in previous editions, the organizers will contribute a workshop summary to the BTW 2027 workshop proceedings capturing the main themes and research directions that emerge.

Contact

For questions, please contact the organizers at ml4sys.sys4mlworkshop@gmail.com.