London, England, United Kingdom
Applying AI techniques to proteomics and clinical data
Maintained and extended large, complex, multi-modal databases for a leading life sciences institution. Back-end (Python’s Django framework) and front-end development (HTML, JavaScript, and CSS).
Tumour Immunogenomics and Immunosurveillance & Cancer Evolution and Genome Instability groups. I use advanced machine learning techniques to integrate high-dimensional, multi-modal biological data to understand tumour-immune relationships and thus identify novel immuno-oncology drug targets. This involves collating, harmonising, and analysing data from patient samples, population-scale biobanks, experimental model systems and biomedical databases. Subsequently, I develop and test diverse algorithms to identify a robust system for target inference. Following target identification, I am experienced in prioritising candidates for further review and functional validation in experimental platforms.
As an Advisor, I am part of the YoungAcademics Core Team. I guide medical students in setting up and running a successful YoungAcademics Division within their university. I have successfully overseen the setting up of a new YoungAcademics branch at the University of East Anglia, Anglia Ruskin University, and the University of Manchester. YoungAcademics is a research collaboration platform which aims to provide medical students access to research projects which are likely to culminate in publications and/or presentations, and provide tutorials to help students develop key research skills.