Biography

I was first introduced to the enormous potential of statistical modelling and machine learning during my undergraduate degree at the LSE and master’s degree at Imperial College. Later on, I worked as a Data Scientist on industry applications in pharma and healthcare, where I noticed a large gap between what machine learning algorithms can do in theory and the degree to which they are adopted in industry. While deep learning models have the potential to help millions of patients during the screening and treatment of fatal diseases, their lack of transparency diminishes the trust of healthcare practitioners, patients, and regulators. I have explored this problem throughout my Computer Science MPhil at Cambridge by studying how we can use design intuitive clinical decision-making tools for different cancer screening tasks. During my PhD, I seek to further develop explainable and stable machine learning systems with a high human-in-the-loop component. I am very excited by the contributions that these systems could bring to the field of healthcare and beyond – improving the accessibility of ML algorithms for fatal diseases and addressing concerns about hidden biases and accountability in algorithmic decision making.