Networks delineate the constituent interactions of a broad range of large-scale complex systems. They are essential to describe socio-economical relations, the human brain, cell metabolism, ecosystems, epidemic spreading, informational infrastructure, transportation systems, and many more.

The structure of these network systems is typically large and heterogeneous. Network theory offers a wide ranging foundation to untangle such intricate systems, potentially allowing us to predict and control their behavior, as well as to provide scientific explanations.

A significant obstacle for the comprehension of such high-dimensional relational objects lies in discerning between signal and randomness. It is crucial to identify which aspects of these systems arise from random stochastic fluctuations and which convey valuable information about an underlying phenomenon. This is a multifaceted problem that often defies intuition, and lies at the heart of any data-driven analysis.

In our group, we focus on the development of principled and trustworthy methods to extract scientific understanding from network data, as well as the mathematical modeling of network behavior and evolution.

Our methods are designed to be robust against overfitting, and to be algorithmically efficient. This is achieved by merging analytical tools and concepts from a variety of disciplines, including Information Theory, Bayesian Statistics, and Statistical Mechanics.

We're particularly interested in problems of network inference where meaningful structural and functional patterns cannot be obtained by direct inspection or low-order statistics, and require instead more sophisticated approaches based on large-scale generative models and efficient algorithms derived from them. In more demanding, but nonetheless ubiquitous scenarios, the network data are noisy, incomplete, or even completely hidden, leaving their trace only via an observed dynamical behavior—in which case the network needs to be fully reconstructed from indirect information.

Most of the methods developed in our group are made available as part of the graph-tool library, which is extensively documented. For a practical introduction to many inference and reconstruction algorithms, please refer to the HOWTO.

I am an Associate Professor in the Department of Network and Data Science at the Central European University (CEU), Vienna, Austria. I have received my Habilitation in Theoretical Physics at the University of Bremen in 2017. Previously, I have been an Assistant Professor in Applied Mathematics at the University of Bath (2016-2019), External Researcher at the ISI Foundation (2015-2020), and post-doc researcher at the University of Bremen (2011-2016) and Technical University of Darmstadt (2008-2011).

The research of my group lies at the interface between Statistical Physics, Complex Systems, Data Science, Applied Mathematics, and Machine Learning, with a special interest in the methodological foundations of Network Science.

Interested PhD candidates are encouraged to apply for the "PhD Program in Network Science at CEU".