I have a diverse career spanning the Information Technology industry and academia, with experience as an engineer, data scientist, analyst, and educator.
I began my professional career at 20 in business management. Following my graduation, I worked with leading UK technology firms and multinational corporations, including Vodafone and Hong Kong Telecoms, as well as software companies across the South East of England. My roles focused on process analysis, systems engineering, and software development.
In 2017, I was awarded the Quintin Hogg Scholarship to pursue my PhD research in data science. By 2020, I joined the university as a data science research fellow, where I investigated data on metabolic health and cancer diagnostics in collaboration with academic partners, NHS Trusts, and cancer centres. In 2021, I joined the teaching team at Westminster. Since then, my work has focused on applied machine learning and data science, with an emphasis on real-world applications. In recognition of my contributions, I was nominated for a Go-Westminster Award in 2024.
Over the past four years, I have initiated multiple projects with students and UK organisations across a wide range of sectors, including healthcare, housing, banking, construction, retail, media and emergency services. Also, I supervise PhD researchers in Data Science and Machine Learning.
My work is anchored by a commitment to applied machine learning and data science as transformative forces for real-world decision-making and societal impact. Over the past four years, I have initiated and led a portfolio of collaborative machine learning projects with students and UK organisations across diverse sectors, including healthcare, housing, banking, construction, retail, media and emergency services. These projects translate data into actionable insight, addressing complex challenges at the intersection of technology, industry, and society. Alongside this work, I supervise PhD researchers in Data Science and Machine Learning, mentoring the next generation of scholars and practitioners who will shape the future of intelligent systems.
Work-Based Learning Projects
• Oxford University Press, UK – Students’ Performance-based Modelling for Assessment Customisation.
• West Midlands Fire Services, UK – Cost of Living Crisis Association Analysis with Domestic Fire Incidents.
• Velocix, UK – Deep Learning Forecasting of Volumetric Server Requests Within Content Delivery Networks.
• AMX Solutions, UK – Machine Learning Modelling of Superstructure Critical Conditions Detection.
• BBC, UK – Machine Learning Predictive Modelling of BBC iPlayer Viewers' Engagement.
• Southern Housing Association, UK – Machine Learning Estimation of Social Housing Energy Efficiency.
• Met Police, UK – Predicting the Effectiveness of ‘Stop and Search’ Police Interventions Using Advanced Data Analytics.
• KPMG, Nigeria – Machine Learning Detection of Corporate and Consumer Banking Transactions Fraud.
• Jayam Cash and Carry, UK – Data Mining Business Transactions for Bulk Buys and Merchandise Planning.
Current PhD Supervisions
Iustina IvanovaThesis: A Data-driven Recommendation Platform For Optimising Sport Climbing Indoors And Tourism Activities. Co-supervisors: Dr Salma Chahed
Ria MukherjeeThesis: Integrating Image-Derived phenotypes, Genomics, and Machine Learning for Predicting Body Fat Composition. Co-supervisors: Dr Salma Chahed, Dr Manuel Corpas and Dr Marjola Thanaj
Yashvi PatelThesis: An Explainable AI Platform for Breast Cancer Risk Assessment and Diagnoses. Co-supervisors: Dr Panagiotis Chountas
Recent Publications
• Aldraimli, M., Osman, S., Grishchuck, D., Ingram, S., Lyon, R., Mistry, A., Oliveira, J., Samuel, R., Shelley, L.E., Soria, D. and Dwek, M.V., 2022. Development and Optimization of a Machine- Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort. Advances in Radiation Oncology, 7(3), p.100890.
• Aldraimli, M., Soria, D., Grishchuck, D., Ingram, S., Lyon, R., Mistry, A., Oliveira, J., Samuel, R., Shelley, L.E., Osman, S. and Dwek, M.V., 2021. A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. Computers in biology and medicine, 135, p.104624.
• Aldraimli, M., Nazyrova, N., Djumanov, A., Sobirov, I. and Chaussalet, T.J., 2020, October. A Comparative Machine Learning Modelling Approach for Patients’ Mortality Prediction in Hospital Intensive Care Unit. In The International Symposium on Bioinformatics and Biomedicine (pp. 16-31). Springer, Cham
• Aldraimli, M., Soria, D., Parkinson, J., Thomas, E.L., Bell, J.D., Dwek, M.V. and Chaussalet, T.J., 2020. Machine learning prediction of susceptibility to visceral fat associated diseases. Health and Technology, 10(4), pp.925-944.
• Aldraimli, M., Soria, D., Parkinson, J., Whitcher, B., Thomas, E.L., Bell, J.D., Chaussalet, T.J. and Dwek, M.V., 2019, September. Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases. In Mediterranean Conference on Medical and Biological Engineering and Computing (pp. 679-693). Springer, Cham.
