Keynote Speakers

Liz Bacon

Abertay University

Professor Liz Bacon FRSE, CEng, CSci, CITP, FBCS, FIScT is Principal and Vice-Chancellor of Abertay University and a Professor of Computer Science with a PhD in Artificial Intelligence. She is a Fellow of the Royal Society of Edinburgh, a National Teaching Fellow, and a Principal Fellow of the Higher Education Academy. She is a past President of BCS, The Chartered Institute for IT, and EQANIE (European Quality Assurance Network for Informatics Education), a past Trustee and Director of Bletchley Park Trust, and a past Chair of CPHC (Council of Professors and Heads of Computing) Committee, and in 2015 was voted the 35th Most Influential Woman in UK IT. Professor Bacon is a worldwide speaker on a range of topics such as the fourth industrial revolution and improving diversity and participation in STEM, particularly among women and people from disadvantaged backgrounds.

Keynote Title: TBD

Abstract: TBD

Elena Simperl

King’s College London

Elena Simperl is a Professor of Computer at King’s College London and the Director of Research for the Open Data Institute (ODI). She is a Fellow of the British Computer Society and the Royal Society of Arts, and a Hans Fischer Senior Fellow. Elena’s work is at the intersection between AI and social computing. She features in the top 100 most influential scholars in knowledge engineering of the last decade and in the Women in AI 2000 ranking. She is the president of the Semantic Web Sciences Association.

Keynote Title: From static portals to intelligent ecosystems: Architecting the future of national open data

Abstract: The traditional open data portal, often a "data graveyard" of fragmented CSVs, is undergoing a radical transformation. As nations race to build robust AI infrastructures, the role of open data has shifted from a transparency requirement to the foundational fuel for sovereign AI. In this talk, I will provide a comprehensive blueprint for these times, drawing on recent projects at King's College London and the Open Data Institute that explore the role of Large Language Models (LLMs) in designing and improving national-scale data architecture. I will show how LLMs are being deployed to solve the two oldest problems in the field: the friction of publishing and the frustration of discovery. Central to this discussion is the concept of "AI readiness", a proposed evaluative framework for determining the suitability of datasets for AI consumption. I will illustrate how we applied the framework to meet the structural and engineering requirements for a National Data Library (NDL). By treating the NDL as a high-availability, AI-ready data product, we outline a reproducible pipeline for leveraging existing ML tools and standards to manage heterogeneous datasets and create nation-wide data assets that aren't just open, but useful, machine-consumable and ethically sound.

Alejandro F Frangi

University of Manchester

Prof Alejandro Frangi is the Bicentenary Turing Chair in Computational Medicine at the University of Manchester and the Royal Academy of Engineering Chair in Emerging Technologies. He leads the UK CEiRSI Centre of Excellence on In Silico Regulatory Science and Innovation and is a Fellow of IEEE, SPIE, and MICCAI. With over 330 journal publications, his work focuses on precision computational medicine and digital twins for medical devices. He is also a co-founder of adsilico Ltd and OculomeX Health Ltd.

Keynote Title: Hippocrates Meets Turing: Digital twins for in silico trials: what works, what breaks, what’s next

Abstract: In silico trials powered by credible digital twins are reshaping medical product evidence, enabling faster, safer, and more affordable innovation by shifting human studies towards confirmatory roles. This talk distils general principles for making this shift work at scale: defining clear contexts of use; building end-to-end pipelines from data curation to anatomy/physiology/device modelling; and rigorously managing verification, validation, and uncertainty quantification to establish model credibility. We will outline how to design proportionate, risk-informed evidence strategies across the product lifecycle, integrate real-world data with physics- and biology-based models (including AI), and operationalise virtual cohorts that capture lifestyle, physiological, and operational envelopes. We will also discuss what breaks—limits of generalisability, failure modes in complex anatomy/physiology couplings, credibility gaps, and socio-technical barriers—and how to mitigate them through standards, best practices, and regulatory “airlock” co-creation. Grounded by brief exemplars from cardiovascular devices, the session looks ahead to scalable infrastructure, workforce development, and global frameworks needed to mainstream trustworthy model-informed evidence.