Workshop at ECML PKDD 2026

1st Workshop on REpresentation learning from Heterogeneous and Multi-source biomEDical data

A focused forum on robust, multimodal, and trustworthy representation learning for real-world clinical data.

07 September 2026 Napoli, Italy Half-day workshop Double-blind review
Submit Paper Call for Papers

About

Representation learning for heterogeneous biomedical data

REHMED 2026 addresses representation learning for heterogeneous biomedical data (EHR, imaging, omics, signals, text), with emphasis on robust and trustworthy clinical AI.

Clinical data are multi-modal, multi-source, longitudinal, and often incomplete. The workshop focuses on methods that improve transferability, robustness, and interpretability across real healthcare settings and institutions.

Special attention is given to uncertainty estimation, calibration, fairness, explainability, and privacy-preserving learning, aligned with high-risk AI requirements in healthcare.

Objectives

  • Advance representation learning for heterogeneous biomedical data.
  • Promote multimodal and weakly supervised approaches.
  • Connect ML/DL innovation with deployment constraints.
  • Foster interdisciplinary collaboration.

Topics

Thematic areas

The workshop welcomes methodological and applied contributions across the following technical areas.

Representation Learning and Adaptation

  • Foundation models for biomedical data
  • Self-supervised and weakly supervised learning
  • Transfer learning and domain adaptation
  • LLMs and ontology-aware representations

Multimodal and Multi-source Integration

  • Cross-modal alignment and fusion
  • Learning from unpaired multimodal data
  • Incomplete and partially observed modalities
  • Data harmonization across institutions

Structured and Temporal Modeling

  • Graph and relational representations
  • Longitudinal patient trajectory modeling
  • Time-series representation learning
  • Geometry-aware and hyperbolic embeddings

Trustworthy, Causal, and Privacy-aware AI

  • Causal representation learning for healthcare
  • Uncertainty estimation and calibration
  • Interpretability, fairness, and safety
  • Federated and privacy-preserving learning

Call for Papers

Submission scope and guidelines

The workshop invites original and unpublished contributions on representation learning methods for heterogeneous biomedical data, with emphasis on methodological rigor and real-world clinical relevance.

What we are looking for

REHMED welcomes methodological advances as well as applications in realistic biomedical settings, including structured EHR, imaging, omics, physiological signals, and clinical text. Contributions may address multimodal fusion, representation learning with limited labels, cross-domain adaptation, causal modeling, or trustworthy AI for healthcare.

The workshop is designed as a forum for machine learning researchers, biomedical data scientists, and clinicians working on robust, transferable, and clinically meaningful representations.

Submission guidelines

  • Original and unpublished work only.
  • Submissions should follow ECML PKDD 2026 workshop track instructions and LNCS formatting guidance.
  • Accepted papers are expected in post-workshop Springer CCIS proceedings, with opt-in or opt-out publication.

Review policy

  • Double-blind reviewing process.
  • At least three independent reviews per submission.
  • Evaluation criteria include relevance, technical rigor, originality, and potential impact.

Technical areas of interest

  • Foundation models, self-supervised, weakly supervised, and contrastive learning.
  • Transfer learning and domain adaptation across tasks, institutions, and populations.
  • Cross-modal learning and integration of EHR, imaging, omics, and clinical text.
  • Graph, temporal, and hierarchical representations for clinical trajectories and biomedical ontologies.
  • Generative and causal approaches for clinically meaningful latent factors and biomarkers.
  • Interpretability, uncertainty, fairness, privacy-preserving learning, and regulation-aware AI.

Workshop format and audience

  • Half-day format combining invited talks, pitch and poster sessions, and moderated discussion.
  • Target audience includes researchers, clinicians, companies, startups, and graduate students.
  • The workshop emphasizes discussion of open problems and future research directions.

Timeline

Important dates

Track-level milestones are from the ECML PKDD 2026 workshop track.

Workshop paper submission deadline

05 June 2026

Notification of acceptance

30 June 2026

Camera-ready submission

10 July 2026

Workshop day

07 September 2026 - Napoli

Publication

Proceedings and formatting

According to ECML PKDD 2026 workshop track guidelines, workshops and tutorials are expected in post-workshop proceedings published by Springer CCIS.

Authors can opt-in or opt-out from proceedings publication, and preparation is suggested in LNCS format.

Selected contributions may also be considered for an extended journal submission route discussed in the workshop proposal.

Speakers

Invited speakers

Invited speakers will be announced when confirmed.

TBA

Organizers

Organizing committee

The workshop brings together researchers with expertise in biomedical AI, trustworthy machine learning, medical imaging, multimodal data integration, and clinical translation.

Alessandro Cacciatore

Alessandro Cacciatore

University of Macerata, Italy

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Program Committee

Program committee

The proposal defines a diverse PC from academia and industry to support the workshop review process.

PC members

  • Domenico Amalfitano, University of Naples Federico II
  • Domenico Benfenati, University of Naples Federico II
  • Angela Crispino, University of Naples Federico II
  • Giovanni Maria De Filippis, University of Naples Federico II
  • Aurora Esposito, University of Molise
  • Francesco Merolla, University of Molise
  • Luca Romeo, University of Macerata
  • Claudio Sirocchi, Università Politecnica delle Marche
  • Maria Chiara Fiorentino, Università degli Studi "G. d'Annunzio" Chieti – Pescara
  • Riccardo Rosati, University of Macerata
  • Rocco Pietrini, Mercatorum University
  • Alessandro Galdelli, Università Politecnica delle Marche
  • Paolo Sernani, University of Macerata

Contact

Workshop contacts