Prof John D. Kelleher


Email Address: [email protected]

Professor Kelleher takes responsibility for further enhancing Trinity’s leadership in the fundamentally important ICT area of AI and provide long-term strategic leadership and vision for the ADAPT Centre in order to achieve and sustain research excellence. Professor Kelleher’s core research expertise is in the areas machine/deep learning and natural language processing.

Professor Kelleher has over two decades of research experience in artificial intelligence, a field that has witnessed significant transformation since the emergence of generative AI.  His research innovations have shown impressive abilities in a range of domains, from healthcare to the sustainability of AI models.

Professor Kelleher’s research interests focus on harnessing AI to enhance the understanding and treatment of complex medical conditions.  For example, his work in stroke care is pioneering, utilising AI and digital twins in the Stratif-AI project to revolutionise risk assessment and enhance prevention, treatment, and rehabilitation throughout a person’s life.  Through the EU-funded Validate project, he’s developing a prognostic tool to predict outcomes for acute stroke patients, translating AI research into vital clinical tools.  Leading the data science efforts in RES-Q+, he contributes to a global initiative aimed at elevating stroke care quality, showcasing his commitment to impactful healthcare improvements through AI.  Additionally Professor Kelleher does a significant amount of research in natural language processing, including work on large-language models and machine translation, with a particular focus on improving the energy and data efficiency of these systems in order to make them more sustainable and applicable in low-resource contexts.

Within the ADAPT centre he leads research projects on language modelling, lexical semantics, machine translation, novelty detection, image captioning, dialog systems, and making AI more environmentally sustainable. John has been the academic lead on numerous industry projects across a range of topics and domains, including: anomaly detection, transfer learning, customer segmentation and propensity modelling, dialog systems and chat bots, and information retrieval and natural language processing.

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Research Domains
  • Artificial Intelligence
  • Data Analytics
  • Deep Learning
  • Human Computer Interaction
  • Machine Learning
  • Machine Translation
  • Multimodal Interaction
  • Natural Language Processing
  • Predictive Analytics