I am a doctoral researcher in the Computational Network Sciences Group at RWTH Aachen University. My research interests include Network Science, Complex Systems and Computational Social Sciences. In particular, I focus on the statistical and dynamical analysis of Social Networks with topics ranging from opinion dynamics on hypergraphs to the effects of systematic errors and bias on rankings in social networks.
I am the Social Secretary of the Women in Network Science Society (WiNS) which aims to foster opportunities for the education, employment, and career advancement of under-represented genders in network science. Reach out to me if you want to participate in events, want to know more about the society or have any other suggestions or requests!
MSc in Mathematical Modelling and Scientific Computing, 2019
University of Oxford
BSc in Mathematics, 2017
University of Bonn
BSc in Psychology, 2021
University of Bonn
[05/08/22] Preprint alert! In our paper “Improving the visibility of minorities through network growth interventions” we find that group size and behavioural interventions need to be coordinated to benefit the ranking position of minorities.
[15/12/21] Our paper on consensus dynamics on temporal hypergraphs has now been published in Physical Review E!
[23/09/21] Now published in Applied Network Science: Our paper on simulating systematic bias in attributed networks. Check it out!
[23/09/21] I gave a talk at NetGCoop 2021 (originally NetGCoop 2020) on our conference paper on Opinion dynamics with Multi-Body Interactions
[13/09/21] New preprint out: It was a pleasure to work on consensus dynamics on temporal hypergraphs with Renaud Lambiotte and Michael Schaub. Check it out!
In this preprint, we investigate consensus dynamics on temporal hypergraphs that encode network systems with time-dependent, multi-way interactions.
In this paper, we introduce a model for systematic edge uncertainty in attributed networks. Our model enables us to distinguish between erroneous edge observations that are driven by external node attributes or the network structure itself, thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.