Bio

I initially wanted to become an oncologist. After spending a short time in an oncology ward, however, I realised that I would not have been able to sustain the psychological burden of clinical practice. What remained with me was both the human motivation behind that choice and the scientific challenge that had drawn me to oncology in the first place.

On the human side, I have always been interested in the possibility of making my professional life contribute, however indirectly, to reducing suffering. Equally important to me is the idea that access to the hope of reducing that suffering should be as equitable as possible, regardless of gender, race, or background, and that patients should be fully seen and represented in their struggle against diseases that are often beyond individual control. From here, my interest in personalised medicine.

On the scientific side, cancer fascinates me as a complex and adaptive system. Tumours are, in a deeply tragic sense, remarkably effective biological systems: they evolve, compete, adapt, exploit environmental constraints, and reorganise under therapeutic pressure. This raises a strategic question that strongly motivates my work: can the very mechanisms that make tumours so resilient be understood deeply enough to be turned against them? And, once I had chosen mathematics as my scientific path, this naturally led to a second question: can mathematics (through its ability to describe, decompose, and analyse complex systems) provide the framework needed to do so?

After training in pure and applied mathematics, and progressively moving during my PhD toward biomathematics and mathematical modelling, I am now developing an independent research direction at the interface between mathematics and oncology. This is a challenging space to occupy, particularly because genuinely interdisciplinary positions are still relatively uncommon, but it is precisely where I want my research to sit.
My approach is to place the mathematical modeller as close as possible to the biological and clinical question. This means understanding which questions are genuinely relevant from a biomedical perspective, identifying the mathematical strategy best suited to address each specific problem, and translating the resulting analysis into conclusions that are interpretable and usable in the application domain.

My main research interests lie in mathematical oncology, with a particular focus on the optimisation and interpretability of therapeutic strategies. I am especially interested in tumour evolution and microenvironment-driven clonal selection, personalised and adaptive treatment strategies, imaging-informed modelling, epigenetic regulation, and tumour plasticity. Ultimately, I aim to develop these themes in a research environment where mathematical modelling, data generation, experimental validation, and clinical relevance are part of the same scientific framework, with continuous interaction between those who formulate the models and those who generate the experiments and observations that give them meaning.