Research

I am a mathematician who studies cancer. My research focuses on developing mathematical, statistical and computational methods to understand and predict cancer dynamics. A selection of research topics that drive my research and the group is listed below.

Quantifying cell-to-cell interactions

Cancer cells interact with each other. We develop mathematical models and Bayesian inference methods to quantify cell-to-cell interactions from cell imaging data. We also evaluate the role that these interactions play in therapy resistance and disease progression.

Estimating biomarker dynamics

Blood-based biomarkers can be used to indicate cancer severity in patients. To improve the predictive abilities of dynamical biomarker models, we develop computational and statistical parameter estimation methods that are designed to handle bio-medical data.

Investigating drug resistance

Drug resistance is a major problem in cancer treatment. We aim to better understand when and why cancer cells (and tumours) become drug resistant. To do this, we use a variety of different modelling methods to study drug resistance on molecular, cellular and tissue-level scales.

Developing Mathematical Oncology tools

Mathematical Oncology is a multidisciplinary research field. We develop computational tools, tutorials and educational materials that simplify mathematical oncology practices for researchers and students from various educational backgrounds.