Dr Farag Shuweihdi
- Position: Senior Statistician/ Lecturer in Medical Statistics & Health Data Science
- Areas of expertise: Machine learning, diagnostic and prognostic prediction models , cluster analysis, multivariate analysis, and meta-analysis.
- Email: F.Shuweihdi@leeds.ac.uk
- Location: 6.090 Worsley Building
- Website: LinkedIn | Googlescholar | Researchgate
Profile
My research experience spans multiple years and focuses on statistical methods, machine learning, diagnostic and prognostic prediction models. I am currently engaged in translational research that involves analysing observational data and large routine databases like electronic patient records and clinical trials. Working closely with clinicians and scientists, I produce applied health research that has a real-world impact. I have published numerous articles in high-impact international journals and have secured funding as a co-applicant for many of my projects. In addition to my research endeavours, I have a passion for teaching and mentorship. I have taught courses in biostatistics at both the graduate and undergraduate levels. I have also supervised a number of PhD and MSc students, and I take great pride in contributing to their academic development through the supervision of theses and capstone projects.
Research interests
My main research interests focus on developing statistical methodologies with real life applications. In particular, I have three broad areas of interest, computational statistics, multivariate analysis and classification. I work on classification approaches, such discriminant analysis, neural network, and decision tree, vector support machine and culstering algorithms. I conduct advanced statistical analysis such as generalized linear models and Survival analysis models, using cross-sectional and longitudinal data. Also, I have excellent experience with analysing structural equation models (SEM).
Research groups and institutes
- Leeds Institute of Health Sciences
- Health Services Research Division
- Health services research