I am a researcher in mathematical oncology, working at the interface of mathematics, statistics, and medicine to address urgent challenges in cancer research. My goal is to develop models that are both mathematically rigorous and clinically meaningful, with direct potential to improve therapeutic strategies and patient outcomes. My research focuses on tumor heterogeneity, considering both the epigenetic characterization of cell subpopulations and the spatial evolution of the mass. I use continuous and hybrid mathematical models, based on ODEs, PDEs, and agent-based approaches, to explore how patient-specific evolutionary and ecological dynamics influence tumor progression and therapy resistance. Within this framework, I study cellular metabolism, the transition from in situ growth to metastasis, and the role of environmental factors in regulating cell behavior. My primary focus is on therapies, from optimizing established strategies such as radiotherapy and chemotherapy to developing innovative approaches like immunotherapy and oncolytic viruses. I am strongly convinced that the impact of mathematical models depends on their validation and I am pushing my research in this direction by integrating quantitative biological information, ranging from epigenetic analyses to tumor imaging, while employing parameter estimation and sensitivity analysis to achieve statistically grounded results. An essential aspect of my work is its interdisciplinary nature: I combine mathematical physics, computational methods, and statistical approaches with biomedical expertise, developing multiscale models that link cellular mechanisms to population-level phenomena. I conduct this research in close collaboration with clinicians and biologists, fostering a dialogue that I firmly believe enriches both sides. Through these interactions, I have also broadened my interests to brain tissue and neurodegenerative processes.