Ruairí O'Reilly

Ruairí O'Reilly

Lecturer in Computer Science at Munster Technological University. Research in explainable and responsible AI, generative AI evaluation, workflow analytics and decision support.

Ruairí O’Reilly

I am a Lecturer in Computer Science at Munster Technological University (MTU), Cork, specialising in explainable and responsible AI, generative AI evaluation, data-driven decision support, and organisational and behavioural analytics. My work bridges artificial intelligence with Information Systems research on decision-making, digital transformation, socio-technical governance, and human–AI collaboration.

I received my B.Sc. (Hons) in Computer Science from University College Cork in 2008 and my Ph.D. in Computer Science from UCC in 2015. My doctoral research developed a distributed architecture for the monitoring and analysis of time-series data.

My current research programme focuses on making AI systems more transparent, reliable and useful in expert decision environments. It spans explainable AI (XAI), fairness and trustworthy AI, generative-model evaluation, workflow analytics, complex systems, information-theoretic modelling, psychophysiological analytics, and biomedical and clinical AI.

I am Programme Coordinator for the M.Sc. in Software Architecture & Design at MTU, a Senior Member of the IEEE, and a member of research communities including Lero, ADAPT, and the Riomh research group.

Current research themes

  • Explainable, trustworthy and fair AI for expert decision support
  • Generative AI evaluation and synthetic-data quality
  • AI-driven workflow analytics and organisational decision-making
  • Complex systems and information-theoretic modelling
  • Human-centred AI, trust and human–AI collaboration
  • Biomedical, clinical and psychophysiological analytics
  • Distributed and reproducible AI/ML workflows

Research, teaching and supervision

My research has contributed to more than €1.75m in funded projects, with €297,300 in funding under my supervision. I have supervised completed PhD and M.Sc. research across XAI, generative AI, biomedical imaging, affective computing and software architecture, together with more than 35 undergraduate final-year projects.

I teach across undergraduate and postgraduate Computer Science programmes. My teaching centres on Knowledge Representation & Reasoning, AI, analytics, programming, data management, software systems and reproducible computational practice, with a strong emphasis on research-led and applied learning.

Recent activity

Recent outputs include work on dataset-agnostic ECG signal-quality assessment, automated Kubernetes persistent-volume scaling, GAN mode-collapse mitigation in biomedical imaging, FAIR-MED for intersectional fairness evaluation, and explanandum-based evaluation of XAI neighbourhoods. In 2026 I served as Special Sessions Chair for the World Conference on Explainable Artificial Intelligence (xAI 2026) and delivered the Pint of Science talk “Agentic AI: Smart Help or Digital Chaos?”.