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Dr Muhammad Salman Haleem

Job Title
Assistant Professor (Research Focussed)
School of Engineering
Research Interests

Machine Learning (incl. of deep learning, traditional machine learning methods etc.)
Data Science
Big Data Analytics
Sequential Data Analytics (e.g. textual data, time series)
Image Processing (incl. medical imaging)
Computer Vision


Dr. Haleem joined University of Warwick as a Senior Research Fellow in August 2020. He has been based in Applied Biomedical Signal Processing and Intelligent eHealth Lab as his research contributions will be primarily towards GATEKEEPER Project. He would be mainly involved in design, development and supervision of novel deep learning based architectures for predicting health trajectory. He is a data scientist who has worked with multiple interdisciplinary research groups in various domains such as biomedical engineering, medical imaging, criminology etc. His research interests include (but not limited to) machine learning, big data analytics, time series analysis, text mining, image processing, computer vision etc.

Previous Research Projects

Big Data for Police Operational Analytics

Previously, he has been Senior Research Associate in Crime and Well-Being Big Data Centre, Manchester Metropolitan University, where he has been involved in the project of 'Policing Operational Analytics' in collaboration with Greater Manchester Police, which aims to analyse evidence-based police demand utilising their 'Big Data'. Specifically, work in this project focus on three themes i) Developing the 'Relational Data Management System' which can integrate data acquired from different police resources (police reports, GPS data, structured police records) as well as socio-economic variables (demographics, landuse etc.) so as to understand causal factors of crime across the city; ii) Development of 'deep learning' based models which can automatically extract contextual insights from police reports which are written in unstructured format describing nature of the incident (e.g. mental-ill health, knife related crime) and to trigger necessary action from the police.; iii) Multilevel modelling so as to understand relationship between police patrolling with the demand generated and area covariates while utilizing the 'Big Data' resource describing police patrolling activity.

Automatic detection of retinal patterns for retinal disease diagnosis

Dr. Haleem finished his PhD in Computing with thesis entitled 'Automatic detection of retinal features to assist retinal disease diagnosis'. Conventionally, retinal disease identification techniques are based on manual observations as clinicians perform basic image operations (changing contrast, zooming in, zooming out etc) manually for determining symptoms associated with retinal diseases. In this regard, his PhD research has been mainly focussed on automating steps in the diagnostic process that could help not only in determining any potential symptom more accurately but also has potential minimizing the time of diagnosis, enabling more patients to be screened and more consistent diagnoses can be achieved in a time efficient manner. Here he developed several novel computer aided methods in order to address the challenges associated such as i) Retinal Area Detector for detecting true retinal area in Scanning Laser Ophthalmoscope images obtained from Optos capable of having 200 field of view, ii) 'Adaptive Region-based Edge Smoothing Model' which determines retinal structure boundary based on supervised classification on multidimensional feature space as well as adjusts it according to energy minimization across irregular boundary points; iii) 'Regional Image Feature Model' performing retinal disease classification after fusing data comprised of morphometric and textural pattern at different spatial region in the retinal scan. This project has been funded by Engineering and Physical Science Research Council (EPSRC-UK) and Optos, plc (Nikon) as well as been selected as a finalist in UK ICT pioneer competition 2015 for the most exceptional thesis. A couple of publications have also been published under this project.

A Task-Based Approach to Parametric Imaging with DCE-MRI

Dr. Haleem has also been involved in a project based on tumour localization and segmentation in the parametric images. The parametric images can be obtained from mathematical modelling of time series data of MRI (Dynamic Contrast Enhanced MRI). The time series data represents the pharmacokinetic activity of contrast agent as they pass through the vasculature system. He determined the weighted model of the time series data by emphasizing the first half and deemphasizing the second half which eventually improved the tumour segmentation performance while constructing the classification performance on parametric images.


  • Finalist in UK ICT Pioneer 2015 competition for most exceptional thesis.
  • Paper entitled "Retinal Area Detector from Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases" selected as a featured article in IEEE Journal of Biomedical and Health Informatics.
  • Recipient of Dorothy Hodgkin Postgraduate Award (DHPA) by Engineering and Physical Sciences Research Council (EPSRC), UK for pursuing PhD.

Selected Publications

For full list, please check google scholar (link)

  1. Allocca, C; Jilal, S; Ail, R; Lee, J; Kim, B; Antonini, A; Motta, E; Schellong, J; Stieler, L; Haleem, M.S.; Georga, E; Pecchia, L; Gaeta, E and Fico, G Towards a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behaviour Guidelines. Applied Sciences (In press).
  2. Haleem, M.S., Castaldo, R., Pagliara, S.M., Petretta, M., Salvatore, M. Franzese, M., Pecchia, L, "Time Adaptive ECG Driven Cardio Vascular Disease Detector" Biomedical Signal Processing and Control: 2021
  3. Langton, S., Bannister, J., Ellison, M., Haleem, M. S., & Krzemieniewska-Nandwani, K. (2021). Policing and mental ill-health: Using big data to assess the scale and severity of, and the frontline resources committed to, mental ill-health related calls-for-service' Policing: 2021
  4. M.Ellison, J.Bannister, W.D.Lee & M.S.Haleem,' Understanding policing demand and deployment through the lens of the city and with the application of big data' Urban Studies: 2021
  5. M.S.Haleem, W.D.Lee, M.Ellison & J.Bannister, ''The 'Exposed' Population, Violent Crime in Public Space and the Night-time Economy'' European Journal on Criminal Policy and Research: 2020
  6. W.D.Lee, M.S.Haleem, M.Ellison & J.Bannister, ''The influence of intra-daily activities and settings upon weekday violent crime in public space'' European Journal on Criminal Policy and Research: 2020
  7. M.S.Haleem, L.Han, P.J.Harding and M.Ellison, "An Automated Text Mining Approach for Classifying Mental-Ill Health Incidents from Police Incident Logs for Data-Driven Intelligence", IEEE International Conference on Systems, Man, and Cybernetics 2019.
  8. M.S.Haleem, L. Han, J. Hemert, B. Li, A. Fleming, "A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis" Journal of Medical Systems, 42(20): 2018.
  9. M.S.Haleem, L. Han, J. Hemert, A. Fleming , L. R. Pasquale, P. S. Silva, B. J. Song, L. P. Aiello "Regional Image Features Model for Classification between Normal and Glaucoma in Fundus and SLO Images" Journal of Medical Systems 40(6): 1-19, 2016
  10. M.S.Haleem, L. Han, J. Hemert, B. Li, A. Fleming, "Retinal Area Detector from Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases". IEEE Journal of Biomedical and Heath Informatics 19(4): 1472-1482, 2015
  11. M.S.Haleem, L. Han, J. Hemert, B. Li, "Automatic Extraction of Retinal Features from Colour Retinal Images for Glaucoma Diagnosis: A review". Computerized Medical Imaging and Graphics 37(7): 581-596, 2013.