The University of Barcelona (UB) is a leading Spanish public institution that offers a broad range of academic programmes and attracts over 60,000 students each year. It employs more than 6,000 teachers and researchers, along with over 2,000 administrative and service staff.
UB is committed to creating a positive societal impact by aligning its activities with the UN Sustainable Development Goals. It maintains strong collaborations with international institutions and is actively engaged in numerous Erasmus+ projects.
UB also prioritises knowledge transfer, fosters synergies with the business sector, and supports a wide range of solidarity and social responsibility initiatives.
Why is UB relevant to AIXPERT?
The University of Barcelona (UB) contributes to the project by leading work package 2 (WP2), “Requirements elicitation and vision for AI Agentic systems,” and by participating in several other key wps. UB’s main responsibilities within WP2 include:
- Reviewing existing models related to AI explainability, trust, and transparency
- Eliciting and analysing user requirements to gain an in-depth understanding of user needs and system features that underpin the project’s overall approach
- Defining the functional, structural, and technical components of the AIXPERT solution through a participatory design methodology
- Developing detailed specifications for the project’s use-case scenarios, ensuring they guide the development and assessment of the tools throughout the entire project lifecycle
- Designing a scalable, modular system architecture and prioritising technical findings that inform the final architecture, interfaces, and integration requirements
- Supporting change-management processes by facilitating early dialogue between user communities, developers, and early adopters
- Contributing to data-governance and evaluation-schema design, including both explicit and implicit evaluation methods
- Defines the overall strategy, planning, and validation framework for the use cases
- Establishes the activity plans and execution timelines for each demonstrator and its related controlled experiments
- Coordinates interactions and dependencies among demonstrators to ensure coherence and effective integration across the project
Meet the Team
Bio
Carlos is a Postdoctoral Researcher at UB working on machine learning for cardiac and multi-organ medical imaging. He completed his PhD in 2024 on automatic cardiac segmentation using deep learning, earning distinctions such as the MyoPS 2020 Best Paper Award at MICCAI. He is also co-founder and CTO of Fedder.ai, a start-up developing federated data infrastructures for trustworthy health AI.
Bio
Bio
Bio
