Ciaran Bench
Research Scientist
Department of Data Science and AI, National Physical Laboratory
My work primarily focuses on uncertainty quantification in deep learning through applications in medicine.
Research Outputs:
Google Scholar
Key Papers
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C. Bench, Uncertainty quantification in deep learning is unsatisfactory for clinical applications and complex decision making, TechRxiv, 2026. (presented at IJCNN 2026)
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C. Bench, Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification, arXiv:2604.17480, 2026.
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C. Bench et al., A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis, Machine Learning: Health, 2, 015011, 2026.
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Academic Service & Outreach
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Special Session Co-Chair, Physics-Informed Neural Networks: Advancements and Applications,
IJCNN 2026, Maastricht, The Netherlands.
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Special Session Chair, Trustworthy and Reliable Artificial Intelligence Applications in Healthcare Decision Making,
IJCNN 2025, Rome, Italy.
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Book Chapter: Aston, Philip J., Adel, Tameem, Bench, Ciaran, Martin, Jörg, Thomas, Spencer A. and Thompson, Andrew. "6 Machine learning tools in metrology". Mathematical and Computational Approaches in Metrology, De Gruyter, 2026, pp. 187-215.
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Medium (@ciaranbench)
