I conduct theoretical and applied research on graph theory, modelling and analysis of dynamic networks, control theory and causal inference. My studies lie at the intersection of three disciplines: computer science, mathematical statistics and applied physics, and find application in areas such as systems biology, medicine, biochemistry, pharmacology, population dynamics and climate physics. I develop theories and mathematical models to infer from experimental data and then to describe the static and dynamic properties of systems that are complex in terms of size, non-linearity, and stiffness. Furthermore, I also develop algorithms to simulate the evolution of these systems over time and their reactions to perturbations from the external environment or induced by the observer. Much of my research on simulation methodologies focuses on Artificial Neural Networks and Bayesian Artificial Neural Networks.