I am a senior researcher at the Chair of Systems Design at ETH Zürich, and hold a Doctorate in Data Science from ETH Zürich.

I explore the resilience and dynamics of complex systems using network science and advanced data-driven modeling. My current focus is on modelling and understanding the resilience of pharmaceutical supply chains using variable length markov chain models and graph theory. Here you can find a press release about our recent work on this topic.

Resilience in Social-Economical Systems

My goal is to understand how social organizations manage and recover from disruptions. This involves developing resilience metrics based on data analysis. Leveraging data science, network science, Bayesian statistics, machine learning, and statistical physics, I create models that reveal the underlying mechanisms of resilience in complex systems.

Statistical Models for Network Data Analysis:

I develop inferential models for analyzing temporal and multi-edge network data, crucial for examining social dynamics. My work includes creating methods for studying repeated interactions within social, economic, and political networks and understanding temporal correlations in network data. A significant part of my research is devoted to the generalized hypergeometric ensemble of random graphs (gHypEG), a model that captures tailored to the study of multi-edge network data, facilitating the inference of significant relationships and the regression analysis of complex networks.

The R package ghypernet offers an Open Source toolkit for employing gHypEG models, enabling researchers to analyze and interpret network data effectively.