Adrian is currently pursuing his PhD at the Technical University of Dresden, following the completion of his Bachelor’s and Master’s degrees at Humboldt University Berlin. His research is centered around the field of causal inference in dynamic time series systems. Specifically, Adrian is dedicated to the task of disentangling causal linear relations from causal nonlinear relations within these systems. This distinction is crucial for accurately modeling and understanding complex time-dependent phenomena. The methods he is developing have wide-ranging applications across various domains. Adrian’s primary focus includes the analysis of time series data related to infectious diseases, where understanding the causal relationships can significantly impact public health interventions. Related to that is also the study of mobility patterns and environmental drivers in populations.