About

I am a Senior Lecturer in Computer Science at the University of Derby, where I lead the Collaborative and Data-centric Engineering Research Cluster and serve as Programme Leader for the MSc (Online) Big Data and Cyber Security programmes. I am also an Honorary Senior Research Fellow at the University of Buckingham.

My research expertise spans Artificial Intelligence, Machine Learning, Computer Vision, Explainable AI, Digital Twins, and Industrial Systems. My work focuses on developing AI-enabled solutions that address real-world challenges and deliver measurable impact across the engineering, railway, aerospace, manufacturing, and healthcare sectors.

I have secured and contributed to several externally funded research projects, including those funded by UKRI Innovate UK, Horizon Europe, East Midlands Investment Zone (EMIZ), and GCRF. I work closely with industry and academic partners nationally and internationally to develop innovative AI solutions. I currently lead a REF2029 UoA11 Impact Case Study focused on translating AI research into deployable technologies that improve safety, operational efficiency, and decision-making in industrial environments.

Between 2018 - 2026, I have supervised five successful PhD completions and three MRes completions, and I currently supervise six doctoral researchers. If you possess a strong passion for pursuing a PhD in deep learning or explainable AI for applications in industry or healthcare and have good programming skills, I encourage you to reach out to me.

Teaching responsibilities

Programme Leadership

Leading the MSc (Online) in Big Data and MSc (Online) in Cybersecurity programmes.

Module Leader

• AI Technology (postgraduate)
• Object Oriented Analysis, Design and Implementation (postgraduate)
• Enterprise Systems (undergraduate)
• Multimedia Security (undergraduate)

Project Supervisor

• Independent Scholarship (MSc Big Data Analytics and IT)
• Independent Studies

PhD Project Supervisor

Completions:

• Sicong Zhang (2026) - Thesis: Breast Lesion and Cancer Sign Segmentation from B-Mode Ultrasound Images: A Two-Stage Approach Based on Deep Learning Neural Networks.
• Anu Bose (2023) - Thesis: Breast Lesion Detection in Ultrasound Images using Deep Neural Networks: Clustering Based Approach for False Positive Reduction
• Tahir Hassan (2023) - Project: Breast and Liver Tumor Classification using Ultrasound Images
• Mohmmad Ahmad (2022) - Thesis: Automatic Convolutional Neural Network Architecture Search for Breast Lesion Classification from Ultrasound Images: An ENAS Bayesian Optimization Approach
• Fakher Mohmmad (2022) - Thesis: Machine Learning Models for Image Faulty-Related Abnormality Recognition in Different Modalities

Current:

• Artificial Intelligence and Digital Twin for Railway Inspection.
• Railway Track Discontinuity Classification using Smartphones on Trains.
• Asset Management and Reliability.
• Combining AI Language Models and Digital-Twins for Systems Design in Rail Transport.
• Artificial Intelligence-Based Diagnosis of Alzheimer's Disease Using Brain MRI Images.
• Is there a role for artificial intelligence in accelerating the learning of radiographers in detecting visual anomalies on a chest x-ray?

Masters by Research Project Supervisor

• Ali Eskandari (2022) - Thesis: Towards CNN Decision Understanding for Breast and Thyroid Lesions Classification from Ultrasound Images
• Yi-Cheng Zhu (2022) - Thesis: Colour Doppler Ultrasound Image Analysis for Thyroid Cancer Classification Using Machine Learning Techniques
• Rahulan Radhakrishnan (2021) - Thesis: Vehicle Pair-Activity Recognition Using Long Short-Term Memory Neural Network

Professional interests

I lead the Collaborative and Data-centric Engineering Research Cluster, whose mission is to collaboratively deliver data-centric and AI-driven innovations, from data capture to actionable insights, that enhance safety, reliability, and efficiency across industrial and societal systems at regional, national, and international levels.

Research interests

My research interests focus on Automated Deep Learning Architecture Design, Explainable Artificial Intelligence (XAI), and Computer Vision, with particular emphasis on industrial applications, digital healthcare, behavioural analysis, and autonomous systems. My work explores the development of intelligent, trustworthy, and data-driven solutions that address real-world challenges across engineering, healthcare, transport, and industrial environments.

Externally funded research projects

• AI for Railway Surface Defects Inspection / Application number: 10100321. UKRI Innovate UK, 2023 - 2027.
• ThrombUS: AI for Ultrasound Monitoring. Horizon Europe, 2026.
• Hydrogen Port Fuel Injection for Retrofit Applications in Diesel Freight Locomotives. East Midlands Investment Zone (EMIZ), 2026.
• Inspector Plus / Application number: 10079284 / Competition: NATEP Helping SMEs innovate in aerospace: Spring 2023. Innovate UK, 2023.
• Deep Learning for Engineering Inspection/ Application number: 10080115 / Competition: Feasibility studies for Artificial Intelligence solutions. Innovate UK, 2023.
• Automatic Cancer Detection. University of Buckingham, 2022.
• Develop drones, robotics systems and remote sensing to help with safer landmine surveying. Global Challenge Research (GCRF), STFC, 2019.

Qualifications

• PhD in Computer Science, University of Lincoln, UK

Recent conferences

1. AlZoubi, A., Eskandari, A., Yu, H., Roullier, B., and McQuade, F., 2026. 3D Reconstruction and Views Clustering for Industrial Objects Inspection. 13th EAI International Conference on Cloud Computing. (Conference 29th June - 1st July, 2026)
2. Aweto, C., Yu, H., and AlZoubi, A., 2026. Generative AI and Digital-Twins for Complex System of Systems Design In Rail Transport System. 13th EAI International Conference on Cloud Computing. (Conference 29th June - 1st July, 2026)
3. Aslam, A., Yu, H., and AlZoubi, A., 2026. Backbone Architecture and Farneback Optical Flow for Weakly-Supervised DVT Detection: A Preliminary Comparative Study. 13th EAI International Conference on Cloud Computing. (Conference 29th June - 1st July, 2026)
4. Bashabsheh, M., Alzubi, M., Alrefai, M. and AlZoubi, A., 2026, March. PID-Based Position Control of a Robotic Arm with Adaptive Gain Tuning. In 2026 International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) (pp. 1-6). IEEE.
5. AlZoubi, A. and Ibrahim, N., 2025, March. A Framework for Decision-Support in Landmine Surveying: Spatio-Temporal Analysis of Land Cover Change Using Sentinel-2. In Research Twinning Conference on Digitalisation and Digital Transformation (pp. 47-53). Cham: Springer Nature Switzerland.
6. AlZoubi, A and Ibrahim, N., 2023. Land Cover Analysis Using Sentinel-2 For Humanitarian Mine Action And ERW Survey. Digital Theme UK-Ukraine Twinning Conference
7. Radhakrishnan, R. and AlZoubi, A., 2022, February. Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition. In International Joint Conference on Computer Vision, Imaging and Computer Graphics (pp. 108-134). Cham: Springer Nature Switzerland
8. Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2022, May. Machine leaning assessment of border irregularity of thyroid nodules from ultrasound images. In Multimodal Image Exploitation and Learning 2022 (Vol. 12100, pp. 50-64). SPIE
9. Hassan, T., Al Zoubi, A., Du, H. and Jassim, S., 2022, May. Ultrasound image augmentation by tumor margin appending for robust deep learning based breast lesion classification. In Multimodal Image Exploitation and Learning 2022 (Vol. 12100, pp. 80-89). SPIE
10. Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2022, May. Machine leaning assessment of border irregularity of thyroid nodules from ultrasound images. In Multimodal Image Exploitation and Learning 2022 (Vol. 12100, pp. 50-64). SPIE
11. Zhang, S., AlZoubi, A. and Du, H., 2022, May. Fully convolutional network for breast lesion segmentation in ultrasound image: towards false positive reduction. In Multimodal Image Exploitation and Learning 2022 (Vol. 12100, pp. 65-79). SPIE
12. Eskandari, A., Du, H. and AlZoubi, A., 2022, April. Clustered-CAM: Visual Explanations for Deep Convolutional Networks for Thyroid Nodule Ultrasound Image Classification. In Medical Imaging with Deep Learning
13. Radhakrishnan, R. and AlZoubi, A., 2022. Vehicle Pair Activity Classification using QTC and Long Short Term Memory Neural Network. In VISIGRAPP (5: VISAPP) (pp. 236-247)
14. Bose, A., Nguyen, T., Du, H. and AlZoubi, A., 2022. Faster RCNN hyperparameter selection for breast lesion detection in 2D ultrasound images. In Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK 20 (pp. 179-190). Springer International Publishing
15. Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2021, June. A generic approach for automatic crack recognition in buildings glass facade and concrete structures. In Thirteenth International Conference on Digital Image Processing (ICDIP 2021) (Vol. 11878, pp. 52-61). SPIE
16. Ibrahim, N., Fahs, S. and AlZoubi, A., 2021, April. Land cover analysis using satellite imagery for humanitarian mine action and ERW survey. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, p. 1173402). SPIE
17. Hassan, T., AlZoubi, A., Du, H. and Jassim, S., 2021, April. Towards optimal cropping: breast and liver tumor classification using ultrasound images. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, pp. 111-122). SPIE
18. Ahmed, M., AlZoubi, A. and Du, H., 2021. Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images. In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings 25 (pp. 438-453). Springer International Publishing
19. Eskandari, A., Du, H. and AlZoubi, A., 2021. Towards linking CNN decisions with cancer signs for breast lesion classification from ultrasound images. In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings 25 (pp. 423-437). Springer International Publishing
20. Ahmed, M., Du, H. and AlZoubi, A., 2020. An ENAS based approach for constructing deep learning models for breast cancer recognition from ultrasound images. Medical Imaging with Deep Learning 2020.
21. Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2020, May. Automatic glass crack recognition for high building façade inspection. In Mobile Multimedia/Image Processing, Security, and Applications 2020 (Vol. 11399, pp. 213-228). SPIE
22. AlZoubi, A. and Nam, D., 2020. Vehicle activity recognition using DCNN. In Computer Vision, Imaging and Computer Graphics Theory and Applications: 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers 14 (pp. 566-588). Springer International Publishing
23. Grenier, A., AlZoubi, A., Feetham, L. and Nam, D., 2018, December. Towards Scene Understanding Implementing the Stixel World. In 2018 IEEE British and Irish Conference on Optics and Photonics (BICOP) (pp. 1-4). IEEE.
24. Fardoulis, J., Kay, S., AlZoubi, A., Aouf, N. and Irshad, R., 2018, April. Robotics and remote sensing for humanitarian mine action & erw survey (rrs-hma). In The 15th International Symposium “MINE ACTION 2018” (pp. 33-37). Government of the Republic of Croatia-Office for Mine Action.
25. AlZoubi, A., Dickinson, P., Pike, T.W. and Al-Diri, B., 2016. Analysing Fish Behaviours Using Three-Dimensional Qualitative Trajectory Calculus. In The Ninth York Doctoral Symposium on Computer Science and Electronics, p.36.
26. AlZoubi, A., Kleinhappel, T.K., Pike, T.W., Al-Diri, B. and Dickinson, P., 2015, March. Solving orientation duality for 3d circular features using monocular vision. In International Conference on Computer Vision Theory and Applications (Vol. 2, pp. 213-219). SCITEPRESS.
27. AlZoubi, A., Kleinhappel, T.K., Pike, T.W., Al-Diri, B., Dickinson, P., 2014. Reconciling Orientation Duality for 3D Circular Features Using Monocular Vision.

Experience in industry

I have more than 8 years of industrial experience in Computer Vision R&D, and GIS as a Senior Software Engineer.

In the media

• Derbyshire Times. (Link) Innovative University of Derby and Sperry Rail inspection project using AI gets underway
• Rail Business Daily. (Link) University of Derby and Sperry Rail launch three-year AI project
• Love Business East Midlands. (Link) Innovative University of Derby and Sperry Rail inspection project using AI gets underway
• FE News. (Link) Innovative University of Derby and Sperry Rail inspection project using AI gets underway
• Rail Forum Feature Article. (Link)
• Deep Learning for Engineering Inspection (DL4EI) – press release announcing project in collaboration with Bloc Digital (rail and road inspection transformation). (Link)

Recent publications

1. Massoud, A., Meziane, F. and AlZoubi, A., A Multi-Dimensional Feedback Engine for Governed Adaptation in Human-in-the-Loop Predictive Maintenance. Results in Engineering, p.110370. https://doi.org/10.1016/j.rineng.2026.110370 
2. AlZoubi, A., Al-Diri, B., Pike, T., Kleinhappel, T. and Dickinson, P., 2017. Pair-activity analysis from video using qualitative trajectory calculus. IEEE Transactions on Circuits and Systems for Video Technology, 28(8), pp. 1109/TCSVT.2017.2701860 .2017.2701860
3. Radhakrishnan, R. and AlZoubi, A., 2025. Explainable Vehicle Activity Recognition: Qualitative and Quantitative Approaches. Applied Artificial Intelligence, 39(1), p.2587505. https://doi.org/10.1080/08839514.2025.2587505 
4. Zhu, Y.C., AlZoubi, A., Jassim, S., Jiang, Q., Zhang, Y., Wang, Y.B., Ye, X.D. and Hongbo, D.U., 2021. A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 110, p.106300. https://doi.org/10.1016/j.ultras.2020.106300 
5. [Book] Machine Learning Technology in Biomedical Engineering, MDPI Bioengineering Special Issue Collection, Edited by Hongqing Yu, Alaa AlZoubi, Yifan Zhao and Hongbo Du, April 2024. https://www.mdpi.com/books/reprint/9182-machine-learning-technology-in-biomedical-engineering 
6. AlZoubi, A., Eskandari, A., Yu, H. and Du, H., 2024. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics. Bioengineering, 11(5), p.453. https://doi.org/10.3390/bioengineering11050453
7. AlZoubi, A., Lu, F., Zhu, Y., Ying, T., Ahmed, M. and Du, H., 2024. Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design. Medical & Biological Engineering & Computing, 62(1), pp.135-149. https://doi.org/10.1007/s11517-023-02922-y 
8. Ahmed, M., Du, H. and AlZoubi, A., 2024. ENAS-B: Combining ENAS with Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification from Ultrasound Images. Ultrasonic Imaging, 46(1), pp.17-28. https://doi.org/10.1177/01617346231208709
9. Han, D., Ibrahim, N., Lu, F., Zhu, Y., Du, H. and AlZoubi, A., 2024. Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images. Ultrasonic Imaging, 46(1), pp.41-55. https://doi.org/10.1177/01617346231200804 
10. Zhu, Y.C., Du, H., Jiang, Q., Zhang, T., Huang, X.J., Zhang, Y., Shi, X.R., Shan, J. and AlZoubi, A., 2022. Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi‐Cohort Study. Journal of Ultrasound in Medicine, 41(8), pp.1961-1974. https://doi.org/10.1002/jum.15873 
11. [Book Chapter]. Radhakrishnan, R. and AlZoubi, A., 2022, February. Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition. In International Joint Conference on Computer Vision, Imaging and Computer Graphics (pp. 108-134). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45725-8_6
12. Kleinhappel, T.K., Al‐Zoubi, A., Al‐Diri, B., Burman, O., Dickinson, P., John, L., Wilkinson, A. and Pike, T.W., 2014. A method for the automated long‐term monitoring of three‐spined stickleback Gasterosteus aculeatus shoal dynamics. Journal of fish biology, 84(4), pp.1228-1233. https://doi.org/10.1111/jfb.12332 
13. AlZoubi, Alaa., 2018. PotDataset. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.5999699
14. Alzoubi, A. and Nam, D., 2018. Vehicle Obstacle Interaction Dataset (VOIDataset). Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.6270233