Prof. Hannah Bast
University of Freiburg, Freiburg, Germany
[Keynote title to be announced]
Abstract
[Abstract to be announced]
Bio
Hannah Bast is a Professor of Computer Science at the University of Freiburg. Her research spans algorithms, information retrieval, and high-performance systems for knowledge graph search and querying. She is known for bridging rigorous computer science with practical, scalable tooling that supports real-world RDF and SPARQL use cases. Her work has contributed to the efficiency and accessibility of large-scale knowledge graph technology, with a strong emphasis on robust engineering and reproducible research.
Prof. Ronald Cornet
Amsterdam UMC & University of Amsterdam, Amsterdam, the Netherlands
Looking in and through the looking-glass
Abstract
This keynote will reflect on 25 years of research in the field of semantics and ontologies for representation and analysis of healthcare data. Addressing technical, social, and governance aspects, the keynote will reflect on progress made and on pitfalls encountered, and will give an outlook on possible ways forward.
Bio
Ronald Cornet is Full Professor of Medical Informatics at Amsterdam UMC (University of Amsterdam). His work focuses on semantic interoperability, terminology, and information modelling as foundations for reusable health data. He has contributed to the design and application of standards and semantic frameworks that enable data-driven healthcare and research, with a consistent emphasis on making clinical and biomedical data more FAIR and fit for trustworthy secondary use.
Prof. Janna Hastings
Idiap Research Institute, Martigny, Switzerland
Knowledge alignment for LLMs: A strategy for reproducibility?
Abstract
Large language models have shown promise for automating data annotation and integration, accelerating knowledge synthesis. However, they perform unreliably for most tasks and lack symbolic grounding or constraints. There is therefore great interest in the potential of combining LLMs with formal ontologies and logical constraints — knowledge alignment — in order to maximise their reliability and guarantee reproducibility. In this presentation, with examples from chemistry and behavioural science, I will discuss current strategies for comprehensive knowledge alignment for LLMs, emerging frontiers and persistent challenges.
Bio
Janna Hastings was born in Cape Town, South Africa where she completed undergraduate studies in Mathematics and Computer Science. Thereafter, she moved to Cambridge, UK to join the Cheminformatics and Metabolism group at the European Bioinformatics Institute (2006–2015) and obtained her PhD in Computational Biology from the University of Cambridge (2015–2019), studying the role of metabolism in healthy ageing using multi-omics data and time-series modelling.
After completing postdoctoral research at UCL, EPFL and Otto-von-Guericke University Magdeburg, from August 2022 she has been Assistant Professor of Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, and Vice-Director of the School of Medicine at the University of St. Gallen. She is also an Associate at the Centre for Behaviour Change at University College London, and Group Leader of the Swiss Institute for Bioinformatics.
From January 2026 she will take up a new position at the Idiap Research Institute, leading the Human-Centred Health AI group. The focus of her current research is on improving current approaches to AI for evidence, biomolecules and decision-making.
Prof. Dipak Kalra
The European Institute for Innovation through Health Data (i~HD)
Scaling up data driven research can leverage the EHDS but needs better quality and more semantically interoperable data
Abstract
The capability to improve the efficiency of clinical trials through the use of real world electronic health record data has scaled up over the past decade, due to the increased richness of EHR data content in structured and coded form, and the market growth of platforms and tools that can analyse hospital data to optimise trial protocol design, case find sites and patients. In parallel over the past decade the scale and sophistication of real world data studies has grown, through national and global federated data networks that can conduct studies with increasing accuracy and speed. The European Health Data Space is set to accelerate the availability and streamline access to large scale real world data, which opens up new opportunities for clinical trials and observational research. This presentation will introduce the EHDS and these imminent opportunities. However, it will also highlight some critical success factors including the importance of knowing the provenance and quality of the source data being analysed, the use of the appropriate data standards, managing metadata correctly and consistently. One of the most challenging issues for the increased reuse of routinely collected data will be ensuring public trust and public support, which will also be examined.
Bio
Dipak Kalra is President of The European Institute for Innovation through Health Data (i~HD) and a leading international figure in electronic health records, interoperability, and the trustworthy reuse of health data. His work has contributed to the development of standards, governance frameworks, and multi-stakeholder initiatives that support responsible data sharing across healthcare and research. He has long championed approaches that combine technical interoperability with ethical, legal, and organisational trust, helping shape practical pathways towards large-scale, high-quality health data ecosystems.