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Professor John Kelleher

Professor of Computer Science (Computer Science)
      
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Professor John Kelleher

Professor of Computer Science (Computer Science)

 JOHN.KELLEHER@tcd.ie

 


I am the Director of the ADAPT Research Center (www.adaptcentre.ie) and the Professor of Computer Science (2016) at the School of Computer Science and Statistics at Trinity College Dublin. My journey in academia began with a BSc. in Computer Applications from Dublin City University in 1997. In 2003, I completed my PhD in Artificial Intelligence, also at Dublin City University, focusing on the intersection of language and vision within the context of situated dialogue. This research studied how humans interact with robots or virtual environments through language, paving the way for advancements in human-computer dialogue systems, and artificial intelligence. Following my doctoral studies, I worked as a post-doctoral researcher at Media Lab Europe and the German Centre for Artificial Intelligence (DFKI). In 2005, I joined the faculty of the School of Computer Science at the Dublin Institute of Technology, later transitioning to Technological University Dublin. In 2017 my research and teaching work was recognized with my appointment as Professor by the Dublin Institute of Technology. I joined the Hamilton Research Institute at Maynooth University as a Professor of Computer Science in 2023. In 2024, I was appointed to the role of Professor of Computer Science at Trinity College Dublin's School of Computer Science and Statistics. Concurrently, I lead the ADAPT Research Center, driving innovation and collaboration in the dynamic field of computer science.
  Artificial Intelligence   Computational Linguistics   Computer Science   deep learning   Health informatics   MACHINE LEARNING   Natural Language Processing
Caglayan, Bora and Wang, Mingxue and Kelleher, John D. and Fei, Shen and Tong, Gui and Ding, Jiandong and Zhang, Puchao, BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 15405 LNCS, 2025, p357 â€" 372 , Notes: [Cited by: 0], Journal Article, PUBLISHED  DOI
Ale, Seun and Hunter, Elizabeth and Kelleher, John D., Correction to: Agent based modelling of blood borne viruses: a scoping review (BMC Infectious Diseases, (2024), 24, 1, (1411), 10.1186/s12879-024-10271-w), BMC Infectious Diseases, 25, (1), 2025, Notes: [Cited by: 0; All Open Access, Gold Open Access], Journal Article, PUBLISHED  DOI
Sardina, Jeffrey and Kelleher, John D. and O'Sullivan, Declan, TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models, 2024 IEEE 18th International Conference on Semantic Computing (ICSC), 2024 IEEE 18th International Conference on Semantic Computing (ICSC), 2024, pp122-129 , Conference Paper, PUBLISHED  DOI
English, Patrick Cormac and Shams, Erfan A. and Kelleher, John D. and Carson-Berndsen, Julie, Following the Embedding: Identifying Transition Phenomena in Wav2vec 2.0 Representations of Speech Audio, IEEE Xplore, ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 14-19 April 2024, IEEE, 2024, pp6685 - 6689, Conference Paper, PUBLISHED  DOI
Jain, A. and Long, P. and Villani, V. and Kelleher, J.D. and Chiara Leva, M., CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation, 2024, pp12993-12999 , Notes: [cited By 0], Conference Paper, PUBLISHED  DOI
English, P.C. and Shams, E.A. and Kelleher, J.D. and Carson-Berndsen, J., FOLLOWING THE EMBEDDING: IDENTIFYING TRANSITION PHENOMENA IN WAV2VEC 2.0 REPRESENTATIONS OF SPEECH AUDIO, 2024, pp6685-6689 , Notes: [cited By 1], Conference Paper, PUBLISHED  DOI
KlubiÄ ka, F. and Kelleher, J.D., ReproHum #1018-09: Reproducing Human Evaluations of Redundancy Errors in Data-To-Text Systems, 2024, pp163-198 , Notes: [cited By 1], Conference Paper, PUBLISHED
Abbas, Ammar N. and Mehak, Shakra and Chasparis, Georgios C. and Kelleher, John D. and Guilfoyle, Michael and Leva, Maria Chiara and Ramasubramanian, Aswin K., Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach, 2024, pp2917 â€" 2923 , Notes: [Cited by: 0; All Open Access, Green Open Access], Conference Paper, PUBLISHED  DOI
English, Patrick Cormac and Kelleher, John D. and Carson-Berndsen, Julie, Searching for Structure: Appraising the Organisation of Speech Features in wav2vec 2.0 Embeddings, 2024, pp4613 â€" 4617 , Notes: [Cited by: 0], Conference Paper, PUBLISHED  DOI
Eduardo Cueto-Mendoza and John D. Kelleher, A framework for measuring the training efficiency of a neural architecture, Artificial Intelligence Review, 57, (349), 2024, p1 - 33, Journal Article, PUBLISHED  TARA - Full Text  DOI
  

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Award Date
Professor of TU Dublin 2017
My research interests and expertise lie at the intersection of Artificial Intelligence (AI), machine learning, natural language processing, and the field of AI for Medicine. I have authored several books in the fields of machine learning and data science, including: "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies" (MIT Press, 2020), co-authored with Brian Mac Namee and Aoife D'arcy; "Deep Learning" (MIT Press, 2019), offering a deep dive into this transformative branch of AI; and "Data Science" (MIT Press, 2018), co-authored with Brendan Tierney, offering an encompassing overview of this dynamic field. In the domain of natural language processing (NLP), my recent focus has been on unraveling the intricacies of large language models, particularly in understanding the types of linguistic information encoded within them. This research often involves probing the vector representations generated by these models. Other topics that I have worked on in this field of natural language processing include machine translation, and the related problem of natural language to source code generation. Examples of recent publications on these topics include: "Following the Embedding: Identifying Transition Phenomena in Wav2vec 2.0 Representations of Speech Audio" (ICASSP, 2024, doi: 10.1109/ICASSP48485.2024.10446494); "Topic Aware Probing: From Sentence Length Prediction to Idiom Identification" (arXiv preprint, 2024, doi: 10.48550/arXiv.2403.02009); "Local or Global: The Variation in the Encoding of Style Across Sentiment and Formality" (International Conference on Artificial Neural Networks, 2023, doi: 10.1007/978-3-031-44204-9_41); "Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space" (Proceedings of the 19th Workshop on Multiword Expressions, 2023, doi: 10.18653/v1/2023.mwe-1.8); and "Adaptive Machine Translation with Large Language Models" (Proceedings of the 24th Annual Conference of the European Association for Machine Translation, 2023, url: https://aclanthology.org/2023.eamt-1.22). My work on AI for Medicine primarily revolves around stroke research. Spanning various aspects including prevention, acute treatment, and rehabilitation, my recent publications in this domain include: "Predictors of social risk for post-ischemic stroke reintegration" (Scientific Reports, 2024, doi: 10.1038/s41598-024-60507-7); "Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis" (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023); "A review of risk concepts and models for predicting the risk of primary stroke" (Frontiers in Neuroinformatics, 2022, doi: 10.3389/fninf.2022.883762); "Age-specific models to capture the change in risk factor contribution by age to short-term primary ischemic stroke risk" (Frontiers in Neurology, 2022, doi: 10.3389/fneur.2022.803749)