Mahmoud Aldraimli

Head and shoulders image of Aldraimli, Mahmoud's profile photo

Lecturer

Computer Science and Engineering

(United Kingdom) +44 20 7911 5000 ext 64680
115 New Cavendish Street
London
GB
W1W 6UW
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About me

Mahmoud, A Quintin Hogg Trust scholar, began his career at the age of 20 in business management. After graduation, he worked with UK technology firms and corporations in process analysis, engineering, and systems development. In 2020, he joined the university as a Data Scientist Research Fellow, working on healthcare research with NHS Trusts, cancer centres, and academic institutions. Now a data science lecturer, Mahmoud’s research focuses on applied machine learning. Recently, he’s initiated machine learning projects with students and UK businesses in housing, healthcare, banking, construction, retail, and media. In 2024, Mahmoud was recognised for his excellence and nominated for a Go-Westminster Award. 

Teaching

Data Mining and Machine Learning.

Information Technology Security

Research

1- 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.

2- 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.

3- 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

4- 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.

5- 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.

Publications

For details of all my research outputs, visit my WestminsterResearch profile.