Nectar Track

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), the flagship European machine learning and data science conference (9-13 September 2024), invites submissions to the Nectar Track.


The goal of the Nectar Track is to offer conference attendees a compact overview of recent scientific advances at the frontier of machine learning and data mining with other disciplines, as already published in related conferences and journals. For researchers from other disciplines, the Nectar Track offers a place to present their work to the ECML-PKDD community and to raise the community’s awareness of AI and data science results and open problems in their field. We invite senior and junior researchers to submit summaries of their own work published in various fields, including but not limited to artificial intelligence, generative AI, big data analytics, bioinformatics, cyber security, games, computational linguistics, natural language processing, information retrieval, computer vision and image analysis, geoinformatics, health informatics, database theory, human-computer interaction, information and knowledge management, robotics, pattern recognition, statistics, social network analysis, theoretical computer science, uncertainty in AI, network science, complex systems science, and computationally oriented sociology, economy and biology, as well as critical data science/studies.


Acceptance criteria will also take into account the publication date (cutoff date—up to 18 months old) and the quality of the conference venue or journal.

Key Dates and Deadlines

Submission Site

Submission Deadline

22 July 2024

Author Notification

12 August 2024

*All deadlines expire on 23:59 AoE (UTC - 12)


Submission Guidelines


Submission Guidelines

Submission site:


Submissions must be extended abstracts of 2-4 pages (including references) and should be based on published work. The corresponding original publication(s) should be clearly indicated. The relevance of the work in the context of machine learning and data mining should be clearly motivated.


Submissions should be formatted according to the Author instructions, style files and copyright form that can be found in the “Lecture Notes in Computer Science” (LNCS) Series. submitted through the conference Microsoft CMT submission site (select from the menu the Nectar track).


Accepted Nectar contributions will be presented as oral presentations but not included in the conference proceedings.


For further information, please see the conference website or contact