Emma Atakpa

My research primarily focuses on the development and validation of statistical models for cancer risk prediction, as well as evaluating personalised cancer prevention and early detection strategies, such as risk-stratified screening. My current work includes the assessment of uptake and adherence to breast cancer endocrine therapy, analysis of cancer patient survival following antibiotic use, and longitudinal modelling of blood biomarkers and symptoms to predict risk of liver cancer.

I hold an MMath in Mathematics with Economics from the University of Sussex and a PhD in Medical Statistics from Queen Mary University of London. My doctoral thesis explored the use of repeated measures of mammographic density to improve breast cancer risk estimation. I have worked both nationally and internationally as a Statistician and Data Scientist at the University of Western Australia and Queen Mary University of London. Additionally, I have experience coordinating cancer audit studies, analysing health inequalities in COVID-19 treatment, and serving as an Independent Statistician on clinical trial data monitoring committees.

Passionate about teaching, I have facilitated modules in statistics and research methods for BSc, MBBS, and MSc programmes, as well as online short courses, at Barts and The London School of Medicine and Dentistry. I co-supervise an Academic Clinical Fellow at the University of Oxford and mentor a member of the HDR UK Health Data Science Black Internship Programme.

I am regularly involved in organising seminars, leading Patient and Public Involvement and Engagement activities, contributing to Equality, Diversity, and Inclusion initiatives, and engaging in science communication with charities such as Breast Cancer Now.