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Staff profile

Overview

Dr Noura Al Moubayed

Associate Professor

Affiliations
AffiliationTelephone
Associate Professor in the Department of Computer Science
Associate Fellow in the Institute of Advanced Study
Fellow of the Wolfson Research Institute for Health and Wellbeing
Fellow of the Wolfson Research Institute for Health and Wellbeing

Biography

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 £6 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.

Research interests

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

Esteem Indicators

Publications

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

  • 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

Journal Article

Supervision students

ChengHao Xiao

Postgraduate Student

Dean Slack

Postgraduate Student

Elizabeth Callander

Postgraduate Student

Jamie Stirling

Postgraduate Student

Jiuxin Wei

Jiyao Pu

Postgraduate Student

Junjie Shentu

Postgraduate Student

Maha Alsayyari

Postgraduate Student

Mohammed Alshammari

Postgraduate Student

Nour Mubarak

Postgraduate Student

Patrick Leask

Postgraduate Student

Sarah Wyer

Postgraduate Student

Sean Farrell

Postgraduate Student

Sid Rao

Postgraduate Student

Sike Xiang

Postgraduate Student

Strahinja Klem

Postgraduate Student

Xingyu Miao

Postgraduate Student

Zainab Bala Kuta