Windridge, Prof. David

Office: TG04, Town Hall Building, Hendon Campus
Email: D.Windridge @ mdx.ac.uk

About

Prof. David Windridge holds the chair of Data Science and Machine Learning at the Department of Computer Science, Middlesex University, London, where he heads the AI and Machine Learning Research Group; he is also research lead for the Department of Computer Science as a whole. His research interests centre on practical and theoretical development in classical and quantum machine learning, generative AI, human-machine teaming & explainable AI. (He also has a former research interest in astronomy/astrophysics, having obtained his PhD in Cosmology at the University of Bristol, UK). He has authored around 200 academic publications in these areas and played a leading role in a number of large-scale machine-learning projects in academic and industrial research settings, as well as having won interdisciplinary research grants across a range of academic areas and application domains. He holds a visiting position at the University of Surrey, UK and sits on the editorial board of the Springer journal Quantum Machine Intelligence.
Heads the University’s Data Science activities.

Research Interests

Machine-learning (A.I.)
Cognitive Systems
Quantum computing
Computer vision

Publications Repository

A machine learning framework for assessing systemic societal vulnerability to coastal flooding

Akindejoye, A., Viavattene, C., Priest, S. and Windridge, D. 2026. A machine learning framework for assessing systemic societal vulnerability to coastal flooding. Journal of Flood Risk Management. 19 (3). https://doi.org/10.1111/jfr3.70249

Margin-aware active learning for user-adaptive text classification

Tirunagari, S., Dhami, M. and Windridge, D. 2026. Margin-aware active learning for user-adaptive text classification. Unger, H. and Meesad, P. (ed.) 9th International Conference on Natural Language Processing and Information Retrieval. Kyushu University, Fukuoka, Japan 12 - 14 Dec 2025 Springer. pp. 447-461 https://doi.org/10.1007/978-3-032-20897-2_33

Judges on trial: the future of research on judicial decision-making

Dhami, M., Kajdanowicz, T., Windridge, D. and Boukacem-Zeghmouri, C. 2026. Judges on trial: the future of research on judicial decision-making. in: Shortland, N. and Alison, L. (ed.) Big Ideas in Forensic Psychology: Visions of a Forensic Future From Leading Voices in the Field Wiley.

Knowledge distillation in the QAOA setting for advantage on quantum hardware

Piperno, S., Windridge, D., Vittori, G., Rosato, A. and Panella, M. 2026. Knowledge distillation in the QAOA setting for advantage on quantum hardware. 2026 IEEE International Symposium on Circuits and Systems (ISCAS). Shanghai, China 24 - 28 May 2026 IEEE. pp. 4854-4858 https://doi.org/10.1109/ISCAS66217.2026.11562409

Domain adaptation in the context of sport video action recognition

Faraji Davar, N., de Campos, T., Windridge, D., Kittler, J. and Christmas, W. 2011. Domain adaptation in the context of sport video action recognition. NIPS 2011 Domain Adaptation Workshop. Sierra Nevada, Spain 17 - 17 Dec 2011

Kernel methods in the age of deep learning: from multimodal fusion to quantum machine learning

Windridge, D. 2025. Kernel methods in the age of deep learning: from multimodal fusion to quantum machine learning. 18th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Ho Chi Minh City, Vietnam 03 - 05 Dec 2025

The application of pre-trained transformer models to UK Court of Appeal legal judgments

Abbas, W., Zia, T., Tirunagari, S., Chennareddy, V., Dhami, M. and Windridge, D. 2025. The application of pre-trained transformer models to UK Court of Appeal legal judgments. Quan, T.T., Sombattheera, C., Pham, H.-A. and Tran, N.T. (ed.) 18th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Ho Chi Minh City, Vietnam 03 - 05 Dec 2025 Singapore Springer. pp. 252-264 https://doi.org/10.1007/978-981-95-4963-4_21

EEL-GA: an evolutionary clustering framework for energy-efficient 3D wireless sensor networks in smart forestry

Batool, F., Ali, K., Lasebae, A., Windridge, D. and Kiyani, A. 2025. EEL-GA: an evolutionary clustering framework for energy-efficient 3D wireless sensor networks in smart forestry. Sensors. 25 (17). https://doi.org/10.3390/s25175250

Identifying social vulnerability profiles for coastal flood using supervised and unsupervised machine learning: a case study of Lekki peninsula, Lagos, Nigeria

Akindejoye, A., Viavattene, C., Priest, S. and Windridge, D. 2025. Identifying social vulnerability profiles for coastal flood using supervised and unsupervised machine learning: a case study of Lekki peninsula, Lagos, Nigeria. International Journal of Disaster Risk Reduction. 127. https://doi.org/10.1016/j.ijdrr.2025.105693

Classical to quantum knowledge distillation: a study on the impact of hybridization

Piperno, S., Vittori, G., Windridge, D., Rosato, A. and Panella, M. 2025. Classical to quantum knowledge distillation: a study on the impact of hybridization. 2025 International Joint Conference on Neural Networks. Rome, Italy 30 Jun - 05 Jul 2025 IEEE. https://doi.org/10.1109/ijcnn64981.2025.11227730

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