I am a machine learning researcher in the healthcare domain.  My research focuses on integrating medical domain knowledge, genetic and clinical data to develop statistical machine learning in different type of clinical prediction models such as 'risk prediction' and 'prediction and personalized medicine' with specific focus on Multimorbidity. 

 I received the BSc (2008) in Telecommunication Engineering and the MSc (2011) degrees in Microelecternics, both with distinction and the PhD (2016) degree Computer Science from the University of Manchester UK.

My career includes over 6 years of experience in the development and application of computational and machine learning methods to clinical data working within multi-disciplinary teams in both NHS and academic settings. 

My MRC skills development fellowship focuses on application of statistical Machine Learning methods for the development of effective preventative strategies for precision medicine.

Precision medicine is the customisation and tailoring of medical interventions to the individual and has the potential to have an enormous impact on healthcare. Precision medicine offers a path to helping people stay healthy for longer, recover from illness faster and avoid wastage of scarce healthcare resources. This potential can only be fully realised if we are able to accurately predict the outcome of patients in different clinical scenarios. The project is funded through a UKRI MRC Skills Development Fellowship involving collaborations with Anne Barton, Niels Peek, Thomas House and John Bowes. 

I am also involved in a multi-disciplinary ARUK project to predict predict PsA (Psoriatic Arthritis) in patients with psoriasis. PsA is a chronic inflammatory arthritis that affects approximately 30% of patients with psoriasis. The ability to predict individuals at high risk of PsA would allow early intervention thus preventing chronic disease and disability.