Staff profile

Dr Noura Al Moubayed is an Associate Professor in Computer Science and Head of ML and AI at Evergreen Life. She leads a research lab of over 15 researchers, advancing cutting-edge machine learning and deep learning solutions with a particular focus on healthcare. Over the past seven years, she has secured more than £8 million in funding across 21 projects from EPSRC, IUK, NIHR, ERDF, and UKRI.

With over 1,500 citations and more than 120 peer-reviewed publications, Dr Al Moubayed's research spans top-tier venues such as ICLR, ICML, ACL, EMNLP, BMJ Oncology, and Nature Scientific Reports, among others. Her research attracted broad media attention, featuring in the BBC, ITV, Time Magazine, Wired, and New Scientist, and in 2019 she was recognised among the top 20 women in AI in the UK by RE•WORK. 

Dr Al Moubayed has over a decade of experience in explainable machine learning, natural language processing, and AI fairness. Her work includes building explainable ML models tailored for predicting organ failure in chemotherapy patients, in collaboration with UCL and UCL Hospitals under a Biomedical Catalyst grant. She also developed explainable models for forecasting A&E admissions and readmissions—research endorsed by NIHR, and presented in the Department of Health and Social Care policy briefing, and awarded Best Talk at the Society for Acute Medicine International Conference 2023. Her leadership in translating AI research into clinical impact reflects her dual academic and applied expertise.

Dr Al Moubayed also serves as an Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence and N8 CIR Machine Learning team lead for Durham where she was named Outstanding Associate Editor for 2024. She also serves as Organiser and co-leads for BioLaySumm Shared Task on Lay Summarisation of Biomedical Research Articles and Radiology Reports at BioNLP, ACL 2025.

Her latest contributions push the frontier of Mechanistic Interpretability in large language models. At ICML 2025,  introduced "Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models", a scalable, data- and compute-efficient alternative to sparse autoencoders (SAEs) for interpreting LLM activations. ITDA maintains over 90% of SAE reconstruction performance and supports robust cross-model comparisons, outperforming SVCCA and CKA in representation similarity tasks. At ICLR 2025, her paper Sparse Autoencoders Do Not Find Canonical Units of Analysis challenges core assumptions in mechanistic interpretability using SAE stitching and meta-SAEs. The findings reveal that existing SAEs fail to yield complete or atomic features, and introduce BatchTopK SAEs to better structure sparse representations. An interactive dashboard of the meta-SAE decompositions is publicly available at https://metasaes.streamlit.app.

In addition, her ACL 2025 (main track) paper "Analyzing LLMs' Cognition of Knowledge Boundary Across Languages Through the Lens of Internal Representation", presents the first cross-lingual analysis of how large language models perceive knowledge boundaries, an essential step toward reducing hallucinations in multilingual settings. By probing internal representations across languages, she shows that knowledge boundary signals are encoded in mid to upper layers and follow a linear cross-lingual structure. Her work introduces a training-free alignment method and demonstrates that bilingual fine-tuning enhances cross-lingual boundary recognition, supported by a newly released multilingual evaluation suite.

• Mechanistic Interpretability
• Machine Learning for Healthcare
• Natural Language Processing
• Multimodal Machine Learning
• Bias and Fairness in Machine Learning
• Explainable Machine Learning

• 2025: Organiser and Co-Lead of the BioLaySumm Shared Task at BioNLP Workshop, ACL:
• 2024: Invited Speaker at the Society for Acute Medicine International Conference:
• 2024: Speaker and a Panellist at The North East Life Sciences Conference:
• 2024: Finalist in NIHR Team Science Camp competition:
• 2024: Named Outstanding Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence:
• 2023: Best Talk Award at the Society for Acute Medicine International Conference:
• 2023: Invited speaker at VentureFest North East:
• 2023: Podcast Interview guest at Make data human podcast:
• 2023: Invited speaker for a specialist lecture at 64th London International Youth Science Forum, Imperial College:
• 2023: Panellist at AI and how it fits into Healthcare – Transforming medicine and improving patient outcomes, Organised by Manchester-pro:
• 2019: Named among the top 30 women in AI in the UK by RE-WORK:
• 2019: ACM-W Inspire Conference Chair:
• 2019: Invited Speaker @ Robert Gordon University, Aberdeen:
• 2019: Organiser of the Computational Neurosciences Special Session: at the 15th Conference on Computability in Europe (CiE 2019)
• 2019: Program Committee member: International Workshop on Social & Emotion AI for Industry
• 2019: Invited Speaker: A Celebration of the University’s Diverse Strengths in Research Symposium
• 2019: Debate Panel member: AI & Society: for better or for worse? 
  Panel of AI and social science experts to discuss the role of artificial intelligence in our society, organised by Durham University and NINE DT
• 2019: Roundtable Panel Member: Machine Learning and Digital Humanities. The event is supported by the Newcastle University Humanities Research Institute (NUHRI) and Animating Text Newcastle University (ATNU)
• 2019: Keynote Speaker at NGSchool: Summer School in Bioinformatics & NGS Data Analysis,
• 2019: Invited Speaker: Unconventional Computation and Natural Computation Conference 2019, Tokyo, Japan
• 2018: ITV news coverage for the pilot study at Fellside Primary School: Discussing how the robotic head 'Robbie', will be used to help children with autism in the future.
• 2018: Sponsorship Chair: 29th British Machine Vision Conference
• 2018: Invited Speaker and Panel Member: 3rd ACM-W UK Inspire Conference
• 2018: Technical Program Committee Chair: ACM Multi Media Conference
• 2018: BBC News coverage for the pilot study at Fellside Primary School: Discussing how the robotic head 'Robbie', will be used to help children with autism in the future.
• 2018: Invited Speaker: Technologies of Crime, Justice and Security Conference
• 2017: Invited Speaker: Re-Work Deep Learning Summit - London
• 2017: Area Chair: Women in Machine Learning Workshop (part of NIPS)
• 2016: Keynote Speaker: NVIDIA's GPU Programming and Machine Learning Workshop 'Deep Learning Applications powered by GPGPUs'
• 2000: Grants Reviewer for the European Commission:
• 2000: Grants Reviewer for EPSRC:

Chapter in book

• In-Materio Extreme Learning Machines
  
  Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (pp. 505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35
• ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference
  
  Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (pp. 460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37
• Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models
  
  Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020. (pp. 633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50
• Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine
  
  Al Moubayed, N., Wall, D., & McGough, A. (2017). Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine. In T. Tryfonas (Ed.), Human aspects of information security, privacy and trust : 5th International Conference, HAS 2017, held as part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, proceedings. (pp. 287-295). Springer Verlag. https://doi.org/10.1007/978-3-319-58460-7_19
• A Novel Smart Multi-Objective Particle Swarm Optimisation using Decomposition
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (2010). A Novel Smart Multi-Objective Particle Swarm Optimisation using Decomposition. In Parallel Problem Solving from Nature, PPSN XI (pp. 1-10). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_1
• Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (n.d.). Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation. In Intelligent Data Engineering and Automated Learning - IDEAL 2011 [Contracted by publisher]. Springer Berlin Heidelberg.
• D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (n.d.). D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance. In Evolutionary Computation in Combinatorial Optimization [Contracted by publisher] (pp. 75-86). Springer Berlin Heidelberg.
• Mutual Information for Performance Assessment of Multi Objective Optimisers: Preliminary Results
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (n.d.). Mutual Information for Performance Assessment of Multi Objective Optimisers: Preliminary Results. In Intelligent Data Engineering and Automated Learning – IDEAL 2013 [Contracted by publisher] (pp. 537-544). Springer Berlin Heidelberg.

Conference Paper

• Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
  
  Leask, P., & Al Moubayed, N. (in press). Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models. In Proceedings of Machine Learning Research.
• Sparse Autoencoders Do Not Find Canonical Units of Analysis
  
  Leask, P., Bussmann, B., Pearce, M., Bloom, J., Tigges, C., Al Moubayed, N., Sharkey, L., & Nanda, N. (in press). Sparse Autoencoders Do Not Find Canonical Units of Analysis. Presented at ICLR2025: The Thirteenth International Conference on Learning Representations, Singapore.
• Sparse Autoencoders Do Not Find Canonical Units of Analysis
  
  Leask, P., Bussmann, B., Pearce, M. T., Isaac Bloom, J., Tigges, C., Al Moubayed, N., Sharkey, L., & Nanda, N. (2025, January 22). Sparse Autoencoders Do Not Find Canonical Units of Analysis. Presented at The Thirteenth International Conference on Learning Representations, Singapore.
• SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
  
  Wu, S., Li, Y., Zhu, K., Zhang, G., Liang, Y., Ma, K., Xiao, C., Zhang, H., Yang, B., Chen, W., Huang, W., Al Moubayed, N., Fu, J., & Lin, C. (2024). SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval. In Findings of the Association for Computational Linguistics: ACL 2024 (pp. 12560-12574). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.746
• Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
  
  Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE. https://doi.org/10.1109/ijcnn60899.2024.10650732
• CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs
  
  Shentu, J., & Al Moubayed, N. (2024). CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 5200-5209). IEEE/CVF. https://doi.org/10.1109/WACV57701.2024.00513
• Addressing Performance Inconsistency in Domain Generalization for Image Classification
  
  Stirling, J., & Moubayed, N. A. (2023). Addressing Performance Inconsistency in Domain Generalization for Image Classification. In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE. https://doi.org/10.1109/ijcnn54540.2023.10191685
• Natural Language Explanations for Machine Learning Classification Decisions
  
  Burton, J., Al Moubayed, N., & Enshaei, A. (2023). Natural Language Explanations for Machine Learning Classification Decisions. In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE. https://doi.org/10.1109/ijcnn54540.2023.10191637
• On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning
  
  Xiao, C., Long, Y., & Al Moubayed, N. (2023). On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning. Presented at Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada. https://doi.org/10.18653/v1/2023.findings-acl.778
• Length is a Curse and a Blessing for Document-level Semantics
  
  Xiao, C., Li, Y., Hudson, G. T., Lin, C., & Al Moubayed, N. (2023). Length is a Curse and a Blessing for Document-level Semantics. Presented at The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore.
• Does lossy image compression affect racial bias within face recognition?
  
  Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022, October). Does lossy image compression affect racial bias within face recognition?. Presented at International Joint Conference on Biometrics (IJCB 2022), Abu Dhabi, UAE.
• Measuring Hidden Bias within Face Recognition via Racial Phenotypes
  
  Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. (2022). Measuring Hidden Bias within Face Recognition via Racial Phenotypes. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI. https://doi.org/10.1109/wacv51458.2022.00326
• Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations
  
  Watson, M., Awwad Shiekh Hasan, B., & Al Moubayed, N. (2022). Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI. https://doi.org/10.1109/wacv51458.2022.00159
• Enhanced Methods for Evolution in-Materio Processors
  
  Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Enhanced Methods for Evolution in-Materio Processors. Presented at IEEE International Conference on Rebooting Computing (ICRC 2021), Virtual. https://doi.org/10.1109/icrc53822.2021.00026
• Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task
  
  Ampomah, I., Burton, J., Enshaei, A., & Al Moubayed, N. (2022). Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (C. Nicoletta, B. Frederic, B. Philippe, C. Khalid, C. Christopher, D. Thierry, G. Sara, I. Hitoshi, M. Bente, M. Joseph, M. Helene, O. Jan, & P. Stelios, Eds.). The European Language Resources Association.
• INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
  
  Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892336
• MuLD: The Multitask Long Document Benchmark
  
  Hudson, G. T., & Al Moubayed, N. (2022). MuLD: The Multitask Long Document Benchmark (N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis, Eds.). The European Language Resources Association.
• Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention
  
  Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan. https://doi.org/10.1109/bigdata55660.2022.10020791
• Efficient Uncertainty Quantification for Multilabel Text Classification
  
  Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892871
• Towards Graph Representation Learning Based Surgical Workflow Anticipation
  
  Zhang, X., Al Moubayed, N., & Shum, H. P. (2022). Towards Graph Representation Learning Based Surgical Workflow Anticipation. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926801
• Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification
  
  Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892257
• A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data
  
  Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual. https://doi.org/10.1109/ijcnn52387.2021.9533981
• Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
  
  Watson, M., & Al Moubayed, N. (2021). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. Presented at The 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy. https://doi.org/10.1109/icpr48806.2021.9412560
• Towards Equal Gender Representation in the Annotations of Toxic Language Detection
  
  Excell, E., & Al Moubayed, N. (2021). Towards Equal Gender Representation in the Annotations of Toxic Language Detection. Presented at 3rd Workshop on Gender Bias in Natural Language Processing (GeBNLP2021), International Joint Conference on Natural Language Processing (INCNLP2021), Bangkok, Thailand. https://doi.org/10.18653/v1/2021.gebnlp-1.7
• Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
  
  Yucer, S., Akcay, S., Al Moubayed, N., & Breckon, T. (2020, June 16). Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation. Presented at Computer Vision and Pattern Recognition Workshops, Seattle, USA.
• On Modality Bias in the TVQA Dataset
  
  Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2020). On Modality Bias in the TVQA Dataset. Presented at The British Machine Vision Conference (BMVC), Manchester, England.
• Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation
  
  Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. In 2019 International Conference on Robotics and Automation (ICRA) ; proceedings. (pp. 4889-4895). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icra.2019.8794060
• Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction
  
  Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. In I. V. Tetko, V. Kůrková, P. Karpov, & F. Theis (Eds.), Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings. (pp. 338-350). Springer Verlag. https://doi.org/10.1007/978-3-030-30493-5_34
• CAM: A Combined Attention Model for Natural Language Inference
  
  Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018). CAM: A Combined Attention Model for Natural Language Inference. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (Big Data) ; proceedings. (pp. 1009-1014). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/bigdata.2018.8622057
• Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers
  
  Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings. (pp. 646-653). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cec.2018.8477779
• An Exploration of Dropout with RNNs for Natural Language Inference
  
  Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018). An Exploration of Dropout with RNNs for Natural Language Inference. In V. Kurková, Y. Manolopoulos, B. Hammer, L. S. Iliadis, & I. G. Maglogiannis (Eds.), Artificial neural networks and machine learning - ICANN 2018 : 27th international Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings. Part III. (pp. 157-167). Springer Verlag. https://doi.org/10.1007/978-3-030-01424-7_16
• Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models
  
  Alhassan, Z., McGough, S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III. (pp. 468-478). Springer Verlag. https://doi.org/10.1007/978-3-030-01424-7_46
• Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
  
  McGough, S., Forshaw, M., Brennan, J., Al Moubayed, N., & Bonner, S. (2018). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. In 2018 Ninth International Green and Sustainable Computing Conference (IGSC). (pp. 1-8). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/igcc.2018.8752115
• On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks
  
  Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018. (pp. 3726-3731). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/smc.2018.00631
• Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data
  
  Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings. (pp. 541-546). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icmla.2018.00087
• Enhanced detection of movement onset in EEG through deep oversampling
  
  Al Moubayed, N., Hasan, B. A. S., & McGough, A. S. (2017). Enhanced detection of movement onset in EEG through deep oversampling. In 2017 International Joint Conference on Neural Networks (IJCNN 2017) : Anchorage, Alaska, USA, 14-19 May 2017. (pp. 71-78). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ijcnn.2017.7965838
• Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems
  
  McGough, A. S., Al Moubayed, N., & M, F. (2017). Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion (ICPE ’17 Companion), April 22 - 26, 2017, L’Aquila, Italy. (pp. 55-60). ACM. https://doi.org/10.1145/3053600.3053612
• SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
  
  Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II. (pp. 423-430). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_50
• Face-Based Automatic Personality Perception
  
  Al Moubayed, N., Vazquez-Alvarez, Y., McKay, A., & Vinciarelli, A. (2014). Face-Based Automatic Personality Perception. In Proceedings of the 22nd ACM international conference on Multimedia - MM ’14, November 03–07, 2014, Orlando, FL, USA. (pp. 1153-1156). Association for Computing Machinery (ACM). https://doi.org/10.1145/2647868.2655014
• Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces
  
  Al Moubayed, N., Awwad Shiekh Hasan, B., Gan, J., Petrovski, A., & McCall, J. (2012). Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces. Presented at 2012 IEEE Congress on Evolutionary Computation, Brisbane, Australia. https://doi.org/10.1109/cec.2012.6252991
• Clustering based leaders' selection in multi-objective evolutionary algorithms
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (2011). Clustering based leaders’ selection in multi-objective evolutionary algorithms (N. Krasnogor, Ed.). ACM. https://doi.org/10.1145/2001858.2001913
• Multi-objective Optimisation of Cancer Chemotherapy using Smart PSO with Decomposition
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (2011). Multi-objective Optimisation of Cancer Chemotherapy using Smart PSO with Decomposition. Presented at 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), Paris, France. https://doi.org/10.1109/smdcm.2011.5949264
• Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces
  
  Al Moubayed, N., Awwad Shiekh Hasan, B., Gan, J., Petrovski, A., & McCall, J. (2010). Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces. Presented at 2010 UK Workshop on Computational Intelligence (UKCI), Colchester, UK. https://doi.org/10.1109/ukci.2010.5625570
• Temporal White-Box Testing Using Evolutionary Algorithms
  
  Al Moubayed, N., & Windisch, A. (2009). Temporal White-Box Testing Using Evolutionary Algorithms. Presented at 2009 International Conference on Software Testing, Verification, and Validation Workshops, Denver, CO. https://doi.org/10.1109/icstw.2009.17
• Signal Generation for Search-Based Testing of Continuous Systems
  
  Windisch, A., & Al Moubayed, N. (2009). Signal Generation for Search-Based Testing of Continuous Systems. Presented at 2009 International Conference on Software Testing, Verification, and Validation Workshops, Denver, CO. https://doi.org/10.1109/icstw.2009.16

Doctoral Thesis

• Multi-objective particle swarm optimisation: methods and applications
  
  Al Moubayed, N. (2014). Multi-objective particle swarm optimisation: methods and applications [Thesis]. Robert Gordon University. https://rgu-repository.worktribe.com/output/248504

Journal Article

• Racial Bias within Face Recognition: A Survey
  
  Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. P. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, 57(4), Article 105. https://doi.org/10.1145/3705295
• The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information
  
  Holland, M., Kellett, J., Boulitsakis-Logothetis, S., Watson, M., Al Moubayed, N., & Green, D. (2025). The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information. Internal and Emergency Medicine. Advance online publication. https://doi.org/10.1007/s11739-024-03828-9
• Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions
  
  Watson, M., Boulitsakis Logothetis, S., Green, D., Holland, M., Chambers, P., & Al Moubayed, N. (2024). Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics, 31(1), Article e101088. https://doi.org/10.1136/bmjhci-2024-101088
• Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities
  
  Farrell, S., Anderson, K., Noble, P.-J. M., & Al Moubayed, N. (2024). Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities. Scientific Reports, 14(1), Article 28763. https://doi.org/10.1038/s41598-024-77385-8
• From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers
  
  Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430
• Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text
  
  Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in Veterinary Science, 11, Article 1352726. https://doi.org/10.3389/fvets.2024.1352726
• Explainable text-tabular models for predicting mortality risk in companion animals
  
  Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1
• Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words
  
  Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S. O., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words. Frontiers in Veterinary Science, 11, Article 1352239. https://doi.org/10.3389/fvets.2024.1352239
• Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function
  
  Chambers, P., Watson, M., Bridgewater, J., Forster, M. D., Roylance, R., Burgoyne, R., Masento, S., Steventon, L., Harmsworth King, J., Duncan, N., & al Moubayed, N. (2023). Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine, 12(17), 17856-17865. https://doi.org/10.1002/cam4.6418
• Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data
  
  Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, 61. https://doi.org/10.1109/tgrs.2023.3273329
• Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making
  
  Boulitsakis Logothetis, S., Green, D., Holland, M., & Al Moubayed, N. (2023). Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Scientific Reports, 13(1), Article 13563. https://doi.org/10.1038/s41598-023-40661-0
• Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation
  
  Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI Open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001
• PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records
  
  Farrell, S., Appleton, C., Noble, P.-J. M., & Al Moubayed, N. (2023). PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records. Scientific Reports, 13(1), Article 18015. https://doi.org/10.1038/s41598-023-45155-7
• Towards Intelligently Designed Evolvable Processors
  
  Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309
• Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels
  
  Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science, 8(e974), Article e974. https://doi.org/10.7717/peerj-cs.974
• ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR
  
  Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing, 60. https://doi.org/10.1109/tgrs.2022.3169455
• Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data
  
  Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6
• A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification
  
  Winterbottom, T., Leone, A., & Al Moubayed, N. (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports, 12(1), Article 13468. https://doi.org/10.1038/s41598-022-15965-2
• Curvature-based feature selection with application in classifying electronic health records
  
  Zuo, Z., Li, J., Xu, H., & Al Moubayed, N. (2021). Curvature-based feature selection with application in classifying electronic health records. Journal of Technological Forecasting and Social Change, 173, Article 121127. https://doi.org/10.1016/j.techfore.2021.121127
• Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records
  
  Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237
• Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study
  
  Zuo, Z., Watson, M., Budgen, D., Hall, R., Kennelly, C., & Al Moubayed, N. (2021). Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study. JMIR Medical Informatics, 9(10). https://doi.org/10.2196/29871
• Ask me in your own words: paraphrasing for multitask question answering
  
  Hudson, G. T., & Al Moubayed, N. (2021). Ask me in your own words: paraphrasing for multitask question answering. PeerJ Computer Science, 7, Article e759. https://doi.org/10.7717/peerj-cs.759
• Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm
  
  Alhassan, Z., Budgen, D., Alshammari, R., & Moubayed, N. A. (2020). Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm. Journal of Medical Internet Research, 8(7), Article e18963. https://doi.org/10.2196/18963
• Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling
  
  Al Moubayed, N., McGough, S., & Awwad Shiekh Hasan, B. (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science, 6, Article e252. https://doi.org/10.7717/peerj-cs.252
• D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces
  
  Al Moubayed, N., Petrovski, A., & McCall, J. (2014). D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces. Evolutionary Computation, 22(1), 47-77. https://doi.org/10.1162/evco_a_00104