Staff profile

Biography

Dr Al Moubayed is an Associate Professor at the department of computer science at Durham University, and Head of Applied Machine Learning and AI at Evergreen Life.

Her main research interest is in Explainable Machine Learning, Natural Language Processing, and Optimisation. Dr Al Moubayed received her PhD from Robert Gordon University, followed by post-doctoral positions at the University of Glasgow and Durham University. Her research projects focus on applying machine learning and deep learning solutions in the areas of healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces. All of which involve high dimensional, noisy and imbalance data challenges.

 Dr Al Moubayed is an Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence and N8 CIR Machine Learning team lead for Durham. She leads multiple projects in collaboration with different industrial partners with a team of over 15 researchers. Her research received several medial coverages (e.g. BBC, ITV, Time Magazine, and Wired Magazine) and she was ranked amongst the top 20 women in AI in the UK by RE•WORK 2019. 

Industrial Collaborators

Furhat Rotobtics
Cievert Ltd
Cardon RMP
WordNerds Ltd
Geoteric Ltd
Geospatial Research Ltd
Caspian Ltd
FOOTY.COM Ltd

• Natural Language Processing
• Machine Learning for Healthcare
• Bias and Fairness in Machine Learning
• Multimodal Machine Learning
• Explainable Machine Learning
• Anomaly Detection
• Social Robotics
• Brain Computer Interfaces
• Evolutionary Computation

• Google Scholar

• 2019: ACM-W Inspire Conference Chair:
• 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: Invited Speaker : Unconventional Computation and Natural Computation Conference 2019, Tokyo, Japan
• 2019: Invited Speaker: A Celebration of the University’s Diverse Strengths in Research Symposium ;
• 2019: Invited Speaker @ Robert Gordon University, Aberdeen:
• 2019: Keynote Speaker at NGSchool: Summer School in Bioinformatics & NGS Data Analysis,
• 2019: Named among the top 30 women in AI in the UK by RE-WORK:
• 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: 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)
• 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
• 2018: Invited Speaker and Panel Member: 3rd ACM-W UK Inspire Conference
• 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: Technical Program Committee Chair: ACM Multi Media Conference
• 2017: Area Chair: Women in Machine Learning Workshop (part of NIPS)
• 2017: Invited Speaker: Re-Work Deep Learning Summit - London
• 2016: Keynote Speaker: NVIDIA's GPU Programming and Machine Learning Workshop 'Deep Learning Applications powered by GPGPUs'
• 0000: Grants Reviewer for EPSRC :
• 0000: Grants Reviewer for the European Commission:

Chapter in book

• Jones, Benedict A. H., Al Moubayed, Noura, Zeze, Dagou A. & Groves, Chris (2022). In-Materio Extreme Learning Machines. In Parallel Problem Solving from Nature - PPSN XVII. Rudolph, Günter, Kononova, Anna V., Aguirre, Hernán Kerschke, Pascal, Ochoa, Gabriela & Tušar, Tea Cham: Springer. 13398: 505-519.
• Gajbhiye, Amit, Al Moubayed, Noura & Bradley, Steven (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V. Farkaš, Igor, Masulli, Paolo, Otte, Sebastian & Wermter, Stefan Springer. 12895: 460-472.
• Gajbhiye, Amit, Winterbottom, Thomas, Al Moubayed, Noura & Bradley, Steven (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In Artificial Neural Networks and Machine Learning – ICANN 2020. Farkaš, Igor, Masulli, Paolo & Wermter, Stefan Springer. 12396: 633-646.
• Al Moubayed, N., Wall, D. & McGough, A. S. (2017). Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine. In 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. Tryfonas, Theo Cham: Springer. 287-295.
• Al Moubayed, N, Petrovski, A & McCall, J (2013). Mutual Information for Performance Assessment of Multi Objective Optimisers: Preliminary Results. In Intelligent Data Engineering and Automated Learning – IDEAL 2013. Springer Berlin Heidelberg. 8206: 537-544.
• Al Moubayed, N, Petrovski,A & McCall,J (2012). D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance. In Evolutionary Computation in Combinatorial Optimization. Springer Berlin Heidelberg. 7245: 75-86.
• Al Moubayed, N, Petrovski, A & McCall, J (2011). Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation. In Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer Berlin Heidelberg. 6936: 100.
• 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. Springer Berlin Heidelberg. 1-10.

Conference Paper

• Zhang, Xiatian, Al Moubayed, Noura & Shum, Hubert P.H. (2022), Towards Graph Representation Learning Based Surgical Workflow Anticipation, 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Ioannina, Greece, IEEE.
• Yu, Jialin, Cristea, Alexandra I., Harit, Anoushka, Sun, Zhongtian, Aduragba, Olanrewaju Tahir, Shi, Lei & Al Moubayed, Noura (2022), Efficient Uncertainty Quantification for Multilabel Text Classification, 2022 International Joint Conference on Neural Networks (IJCNN). Padova, Italy, IEEE.
• Yu, Jialin, Cristea, Alexandra I., Harit, Anoushka, Sun, Zhongtian, Aduragba, Olanrewaju Tahir, Shi, Lei & Al Moubayed, Noura (2022), INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations, 2022 International Joint Conference on Neural Networks (IJCNN). Padova, Italy, IEEE.
• Sun, Zhongtian, Harit, Anoushka, Cristea, Alexandra I., Yu, Jialin, Shi, Lei & Al Moubayed, Noura (2022), Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification, 2022 International Joint Conference on Neural Networks (IJCNN). Padova, Italy, IEEE.
• Hudson, G Thomas & Al Moubayed, Noura (2022), MuLD: The Multitask Long Document Benchmark, in Calzolari, Nicoletta, Bechet, Frederic Blache, Philippe Choukri, Khalid, Cieri, Christopher, Declerck, Thierry Goggi, Sara, Isahara, Hitoshi, Maegaard, Bente, Mariani, Joseph, Mazo, Helene, Odijk, Jan & Piperidis, Stelios eds, International Language Resources and Evaluation Conference. Marseille, European Language Resources Association (ELRA), 3675‑3685.
• Jones, Benedict A. H., Al Moubayed, Noura, Zeze, Dagou A. & Groves, Chris (2022), Enhanced Methods for Evolution in-Materio Processors, IEEE International Conference on Rebooting Computing (ICRC 2021). Virtual, IEEE.
• Watson, M., Awwad Shiekh Hasan, B. & Al Moubayed, N. (2022), Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations, Proc. Winter Conference on Applications of Computer Vision. Waikoloa, HI, IEEE.
• Yucer, S., Tekras, F., Al Moubayed, N. & Breckon, T.P. (2022), Measuring Hidden Bias within Face Recognition via Racial Phenotypes, Proc. Winter Conference on Applications of Computer Vision. Waikoloa, HI, IEEE.
• Yucer, S., Poyser, M., Al Moubayed, N. & Breckon, T.P. (2022), Does lossy image compression affect racial bias within face recognition?, International Joint Conference on Biometrics (IJCB 2022). Abu Dhabi, IEEE.
• Sun, Zhongtian, Harit, Anoushka, Cristea, Alexandra I., Yu, Jialin, Al Moubayed, Noura & Shi, Lei (2022), Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention, IEEE Big Data. Osaka, Japan, IEEE.
• Ampomah, Isaac Burton, James Enshaei, Amir & Al Moubayed, Noura (2022), Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task, in Calzolari, Nicoletta, Bechet, Frederic Blache, Philippe Choukri, Khalid, Cieri, Christopher, Declerck, Thierry Goggi, Sara, Isahara, Hitoshi, Maegaard, Bente, Mariani, Joseph, Mazo, Helene, Odijk, Jan & Piperidis, Stelios eds, 13th Conference on Language Resources and Evaluation (LREC 2022). Marseille, European Language Resources Association, 3542‑3551.
• Sun, Zhongtian, Harit, Anoushka, Yu, Jialin, Cristea, Alexandra & Al Moubayed, Noura (2021), A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data, 2021 International Joint Conference on Neural Networks (IJCNN). Shenzhen, China, IEEE.
• Excell, Elizabeth & Al Moubayed, Noura (2021), Towards Equal Gender Representation in the Annotations of Toxic Language Detection, 3rd Workshop on Gender Bias in Natural Language Processing (GeBNLP2021), International Joint Conference on Natural Language Processing (INCNLP2021). Bangkok, Thailand, ACL, 55-65.
• Watson, Matthew & Al Moubayed, Noura (2021), Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning, The 25th International Conference on Pattern Recognition (ICPR2020). Milan, Italy, IEEE.
• Winterbottom, T, Xiao, S, McLean, A & Al Moubayed, N (2020), On Modality Bias in the TVQA Dataset, The British Machine Vision Conference (BMVC). Manchester, UK.
• Yucer, S, Akcay, S, Al Moubayed, N & Breckon, T.P (2020), Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation, Computer Vision and Pattern Recognition Workshops. Seattle, USA, IEEE.
• Alhassan, Zakhriya, Budgen, David, Alessa, Ali, Alshammari, Riyad, Daghstani, Tahini & Al Moubayed, Noura (2019), Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction, in Tetko, Igor V. Kůrková, Věra Karpov, Pavel & Theis, Fabian eds, Lecture Notes in Computer Science 11731: 28th International Conference on Artificial Neural Networks, ICANN2019. Munich, Germany, Springer, Cham, 338-350.
• Aznan, N.K.N., Connolly, J.D., Al Moubayed, N. & Breckon, T.P. (2019), Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation, 2019 IEEE International Conference on Robotics and Automation (ICRA). Montreal, Canada, IEEE, 4889-4895.
• Gajbhiye, Amit, Jaf, Sardar, Al-Moubayed, Noura, Bradley, Steven & McGough, A. Stephen (2018), CAM: A Combined Attention Model for Natural Language Inference, in Abe, Naoki Liu, Huan Pu, Calton Hu, Xiaohua Ahmed, Nesreen Qiao, Mu Song, Yang Kossmann, Donald Liu, Bing Lee, Kisung Tang, Jiliang He, Jingrui & Saltz, Jeffrey eds, IEEE International Conference on BIG DATA. Seattle, United States of America, IEEE, Piscataway, N.J., 1009-1014.
• Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D.A., Petty, M.C. & Al-moubayed, N. (2018), Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers, 2018 IEEE World Congress on Computational Intelligence (WCCI 2018). Rio de Janeiro, Brazil, IEEE, Piscataway, 646-653.
• Gajbhiye, Amit, Jaf, Sardar, Al-Moubayed, Noura, McGough, A. Stephen & Bradley, Steven (2018), An Exploration of Dropout with RNNs for Natural Language Inference, in Kurková, V., Manolopoulos, Yannis, Hammer, Barbara, Iliadis, Lazaros S. & Maglogiannis, Ilias G. eds, Lecture Notes in Computer Science, 11141 ICANN 2018: 27th International Conference on Artificial Neural Networks. Rhodes, Springer, Cham, 157-167.
• Alhassan, Zakhriya, McGough, Stephen, Alshammari, Riyad, Daghstani, Tahini, Budgen, David & Al Moubayed, Noura (2018), Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models, in Kůrková, Věra, Manolopoulos, Yannis, Hammer, Barbara, Iliadis, Lazaros & Maglogiannis, Ilias eds, Lecture Notes in Computer Science 1141: International Conference on Artificial Neural Networks (ICANN). Rhodes, Greece, Springer, 468-478.
• Alhassan, Zakhriya, McGough, A. Stephen, Alshammari, Riyad, Daghstani, Tahani, Budgen, David & Al Moubayed, Noura (2018), Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data, IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018). Orlando, Fl, USA, IEEE, 541-546.
• Aznan, N.K.N., Bonner, S., Connolly, J.D., Al Moubayed, N. & Breckon, T.P. (2018), On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018). Miyazaki, Japan, IEEE, Piscataway, NJ, 3726-3731.
• McGough, S Forshaw, M, Brennan,J Al Moubayed, N & Bonner, S (2018), Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments, 9th International Green and Sustainable Computing Conference. Pittsburgh, PA, US, IEEE, 1-8.
• Al Moubayed, Noura, Hasan, Bashar Awwad Shiekh & McGough, Andrew Stephen (2017), Enhanced detection of movement onset in EEG through deep oversampling, 30th International Joint Conference on Neural Networks (IJCNN 2017). Anchorage, Alaska, USA, IEEE, Piscataway, 71-78.
• A. S, McGough, N, Al Moubayed & M, Forshaw (2017), Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems, ICPE '17 Companion 3rd International Workshop on Energy-aware Simulation (ENERGY-SIM’17). L'Aquila, ACM, New York, 55-60.
• Al Moubayed, N., Breckon, T.P., Matthews, P.C. & McGough, A.S. (2016), SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder, in Villa, Alessandro E.P., Masulli, Paolo & Pons Rivero, Antonio J. eds, Lecture Notes in Computer Science 9887: Springer International Publishing, Cham, 423-430.
• Al Moubayed, N., Vazquez-Alvarez, Y., McKay, A. & Vinciarelli, A. (2014), Face-Based Automatic Personality Perception, MM '14 22nd ACM international conference on Multimedia - MM '14. Orlando, Florida, USA, Association for Computing Machinery (ACM), New York, NY, USA, 1153-1156.
• Al Moubayed, N Awwad Shiekh Hasan, B Gan, J Q Petrovski, A & McCall, J (2012), Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces, 2012 IEEE Congress on Evolutionary Computation. Brisbane, Australia, IEEE, 1-7.
• Al Moubayed, N, Petrovski, A & McCall, J (2011), Clustering based leaders' selection in multi-objective evolutionary algorithms, Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11. Dublin, Irland, ACM.
• Al Moubayed, N, Petrovski, A & McCall, J (2011), Multi-objective Optimisation of Cancer Chemotherapy using Smart PSO with Decomposition, 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM). Paris, France, IEEE, 81 - 88.
• Al Moubayed, N Awwad Shiekh Hasan, B, Gan, J Q Petrovski, A & McCall, J (2010), Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces, 2010 UK Workshop on Computational Intelligence (UKCI). Colchester, UK, IEEE, 1-6.
• Al Moubayed, N & Awwad Shiekh Hasan, B (2009), Temporal White-Box Testing Using Evolutionary and Search-base Algorithms, 9th Annual Workshop on Computational Intelligence. Colchester, UK.
• Al Moubayed, N & Windisch, A (2009), Temporal White-Box Testing Using Evolutionary Algorithms, 2009 International Conference on Software Testing, Verification, and Validation Workshops. Denver, CO, IEEE, 150.
• Windisch, A & Al Moubayed, N (2009), Signal Generation for Search-Based Testing of Continuous Systems, 2009 International Conference on Software Testing, Verification, and Validation Workshops. Denver, CO, IEEE.

Doctoral Thesis

• Al Moubayed, N (Accepted). Multi-objective particle swarm optimisation: methods and applications. Robert Gordon University. PhD.

Journal Article

• Yu, Jialin, Cristea, Alexandra I., Harit, Anoushka, Sun, Zhongtian, Aduragba, Olanrewaju Tahir, Shi, Lei & Al Moubayed, Noura (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI Open
• Kluvanec, Daniel, McCaffrey, Kenneth J. W., Phillips, Thomas B. & Al Moubayed, Noura (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
• 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.
• Jones, Benedict A. H., Chouard, John L. P., Branco, Bianca C. C., Vissol-Gaudin, Eléonore G. B., Pearson, Christopher, Petty, Michael C., Al Moubayed, Noura, Zeze, Dagou A. & Groves, Chris (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation 30(4): 479–501.
• Winterbottom, Thomas, Leone, Anna & Al Moubayed, Noura (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports 12(1): 13468.
• Winterbottom, Thomas, Xiao, Sarah, McLean, Alistair & Al Moubayed, Noura (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science 8: e974.
• Shakeel, Anza, Walters, Richard J, Ebmeier, Susanna K & Moubayed, Noura Al (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing 60.
• Hudson, G. Thomas & Al Moubayed, Noura (2021). Ask me in your own words: paraphrasing for multitask question answering. PeerJ Computer Science 7: e759.
• Zuo, Zheming, Li, Jie, Xu, Han & Al Moubayed, Noura (2021). Curvature-based feature selection with application in classifying electronic health records. Journal of Technological Forecasting and Social Change 173: 121127.
• Alhassan, Zakhriya, Watson, Matthew, Budgen, David, Alshammari, Riyad, Alessa, Ali & Al Moubayed, Noura (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics 9(5): e25237.
• Zuo, Zheming, Watson, Matthew, Budgen, David, Hall, Robert, Kennelly, Chris & Al Moubayed, Noura (2021). Data Anonymization for Pervasive Healthcare: A Systematic Mapping Study. JMIR Medical Informatics
• Alhassan, Zakhriya, Budgen, David, Alshammari, Riyad & Moubayed, Noura Al (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): e18963.
• Al Moubayed, Noura, McGough, Stephen & Awwad Shiekh Hasan, Bashar (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science 6: e252.
• 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.