BioCARLA Workshop- Challenges and Advances in Small/Foundation Models for Biomedical and Life Sciences

Workshop Description

The 7th edition of BioCARLA continues to serve as a central forum within the CARLA conference series (carlaconference.org), bringing together experts in biology, biomedical engineering, computer science, and AI. This year, the workshop focuses on the growing impact of small and foundation models (FMs) in advancing biomedical research, healthcare innovation, and life science applications. Participants will explore how these models can bridge the gap between cutting-edge machine learning and real-world clinical or biotech needs.
The BioCARLA Workshop, part of the CARLA conference series, is a key forum for researchers applying HPC, machine learning, and big data analytics to bioinformatics and biomedicine. The workshop focuses on two core challenges: 1. Transforming multi-omics and biomedical data into actionable insights using scalable HPC-powered models. 2. Advancing clinical readiness through secure, trustworthy, interpretable, and deployable AI systems.
BioCARLA 2025 will spotlight cutting-edge research and interdisciplinary efforts to develop practical, secure, and accessible computational tools for the biomedical and life sciences.

Call for papers

BioCARLA invites submissions from researchers in biology, biomedicine, and computer science working in multidisciplinary teams or startups. We seek original research that integrates HPC, ML, big data analytics, and large-scale multi-omics datasets.

Submissions should present novel findings or innovative solutions to current challenges.

Presentation Formats:

Submission Guidelines

All submitted works must adhere to the general guidelines for papers and posters outlined for CARLA 2025. (https://carlaconference.org/call-papers/)

Topics

• Foundation and practical application to integrate Bioinformatics, System Biology, and HPC
• Design, implementation, and integration of biological workflow using HPC technologies
• Network biological and evolutionary methods for multi-omics integration
• Computational support of Bioinformatics analyses in parallel and distributed environments
• Best practices in HPC management and development for Bioinformatics and System Biology
• HPC, Big Data Analytics and Integration for Multi-Omics Biomedical Data
• Bioinformatics training activities – computational genomics, metagenomics, phylogenomic, proteomics, and proteogenomic – applied in HPC technologies
• Predictive modeling of risks, diseases, and patients’ outcomes using machine learning and deep learning techniques.

Chairs

• John Garcia-Henao, Balgrist University Hospital, Switzerland, John.GarciaHenao@balgrist.ch
• Kary Ocaña, National Laboratory of Scientific Computing, Brazil, karyann@lncc.br