Since my PhD studies, my research has focused on modeling chromosome structure and dynamics using tools and methods from polymer physics and computer simulations. Since 2020, I have been building new skills in bioinformatics for the analysis of transcriptomics, epigenomics, genomics, and 3D genomics data, which I used to inform and contextualize my modelling strategies. Within the team, we share similar research interests, but my expertise is largely orthogonal to that of my colleagues: having complementary, interdisciplinary expertise is a valuable resource for addressing complex biological questions. Over the last years, I have investigated a wide variety of biological questions using bottom-up (hypothesis-driven) methods that aim to build parametric, predictive models based on mechanistic rules inferred from data and experimental observations. In close collaboration with experimental biologists of the team, we develop synergistic approaches that trigger a virtuous cycle: experiments help refine mechanistic rules and improve models, while models help test these new mechanisms and generate new hypotheses. My main interests and contributions have focused on understanding chromatin regulation and 3D structural organization in eukaryotes, with a particular focus on identifying regulators of 3D genome memory, critical processes that trigger epigenetic memory alterations, and the functional consequences for gene expression regulation. By developing models and computer simulations at multiple scales, my work contributes to better characterizing the dynamical coupling among gene expression, chromatin regulation, and 3D genome organization, as well as studying novel biophysical phenomena.