Our research lies in mathematical biology with a special focus on multi-scale infectious disease modeling. Adopting mechanistic approaches, the aim is to develop a deeper quantitative understanding of how system behavior emerges from the interaction of its components, and how processes at one biological scale affect patterns we observe at another. At the genetics-ecology-epidemiology interface, we study processes from the individual to the population level. Data-driven mathematical modeling is applied to a variety of topics, including antibiotic resistance management, multi-type pathogen ecology, within-host interactions in health and disease, and evolutionary diversification. We combine analysis with computer simulations, and we are particularly interested in dynamical systems, stochastic processes and Bayesian inference. The insights gained from our interdisciplinary research can have practical implications for medical settings and public health policy, and provide innovative frameworks for the interpretation of biological data.