Professorial Lecture Series 2026: Artificial Intelligence & Data Science

Professor Ivan Olier

Professor Ivan Olier is Head of the Artificial Intelligence and Digital Technologies Research Institute (AIDTRI) and Head of Research in the School of Computer Science and Mathematics at LJMU. He was appointed Professor of Artificial Intelligence and Data Science in 2024, having joined LJMU in 2018 as a Senior Lecturer and progressing to Reader in 2023.

Since joining LJMU, he has played a key role in developing research and teaching in AI and data science, including winning a highly selective industrial datathon organised by the Portuguese energy company Galp Energia and establishing a collaborative MSc in Data Science with the University of Bahrain. He is co-PI of TARGET, among the largest research consortia led by LJMU to date, and leads the AI work package in ARISTOTELES, both funded by the EU Horizon programme. In 2025, he received the Vice-Chancellor’s Award for Excellence in Research. He also co-founded AIDTRI as the LJMU’s focal point for AI and digital technologies research and knowledge exchange.

He previously held positions at Keele University, the University of Manchester, and the Autonomous University of Barcelona. He completed his PhD in Artificial Intelligence at the Technical University of Catalonia. His research focuses on causal AI, digital twins, and the modelling of complex, structured data, with applications in healthcare and related domains.

Prediction isn’t enough: reflections on AI, data, and decision-making

"This inaugural lecture reflects on the path my research has taken, from early work in probabilistic modelling to more recent efforts to develop AI systems that can engage with complex, real-world problems. During my PhD, I focused on probabilistic approaches for analysing high-dimensional and temporal data, which shaped my interest in how structure can be learned from uncertainty.

Over time, I became increasingly interested in the limits of prediction, and in how models might transfer knowledge across tasks, adapt to new settings, and move towards reasoning about cause rather than correlation. This led me to work on meta-learning, causal inference, and, more recently, digital twins and explainable AI.

In this lecture, I reflect on how these strands connect, and on the challenge of translating methods into real impact, particularly in high-stakes settings such as medicine, drug design and astrophysics. The real value of AI lies not just in prediction, but in helping us understand complex systems and make better decisions."

Professor Sandra Ortega-Martorell

Professor Ortega-Martorell is Professor of Data Science at LJMU, specialising in the development and translation of Artificial Intelligence (AI) and Machine Learning (ML) into real-world applications, particularly in healthcare. Her research addresses major national and global challenges, including ageing, cancer, and cardiovascular disease.

She joined LJMU as a Marie Curie Research Fellow after completing her PhD in Computer Science at the Autonomous University of Barcelona in 2012. She was appointed Senior Lecturer in Data Science in 2016, promoted to Reader in 2023, and to Professor in 2024. She leads the CORE-AI Research Group (Core Methods and Applications of AI) within the Artificial Intelligence and Digital Technologies Research Institute (AIDTRI).

Professor Ortega-Martorell is the lead of the Horizon Europe project TARGET, which develops AI and digital twin technologies to support early diagnosis, personalised management, and rehabilitation of patients with atrial fibrillation-related stroke. Her work advances AI-driven approaches in cardiovascular medicine, thrombosis, and critical care, with a strong emphasis on translating methodological innovation into clinical impact.

From models to impact: Translating AI into real-world healthcare

"Artificial intelligence has made remarkable advances in modelling complex biomedical data, yet a persistent gap remains between methodological innovation and real-world impact. In this lecture, I reflect on my research journey from early work in biomedical data analysis to recent contributions to personalised risk modelling and digital twin technologies in cancer, cardiovascular medicine, and critical care.

A central theme of my work is translation: moving beyond model development towards clinically meaningful and trustworthy systems. Drawing on applications in cancer, atrial fibrillation, stroke, and intensive care, I will show how integrating diverse data sources and clinical knowledge can help address complexity, heterogeneity, and multimorbidity in patient care.

I will argue that the key challenge is not only to build accurate models, but to embed them within healthcare systems; bridging data, expertise, and decision processes. This perspective reframes AI not as an end in itself, but as a catalyst for change in how care is understood and delivered."

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