About me

In my research, I combine mathematical and computational modeling with statistical inference and machine learning to better understand host-pathogen interactions at both the within-host and population level.

Broadly speaking, my interests involve inference, prediction, and control of complex dynamical systems in biology. More specifically, I focus on the development of immune responses after viral infections; HIV infection dynamics in the context of antiretroviral treatment and remission; spread, control and evolution of pathogens on the population level. The cornerstone of my research is the development of dynamical and statistical models. Data in the life sciences is often complex, multi-modal, and generated by out-of-equilibrium processes, therefore requiring formal modeling to gain comprehensible insights. I enjoy using a variety of modeling techniques such as stochastic modeling, Bayesian inference, deep learning, and agent-based models. My research is often done in close collaboration with the experimental scientists that provide the data.