Dominik Schinder is a Ph.D. candidate in Applied Mathematics at Imperial College London, supervised by Professor Mauricio Barahona. Prior to the Ph.D., he received an MSc in Applied Mathematics from Imperial College and an MA in Digital Media from Goldsmiths, University of London. Drawing from Network Science and Machine Learning, he develops mathematical tools for the Computational Social Sciences. During his research stay at the Weizenbaum Institute he will use these methods to analyse the diffusion of far-right conspiracy theories across different social media platforms.
Former Research Fellow
Research Group “Dynamics of Digital Mobilization“, 1.6.-31.7.2023
Research Group “Digitalisation and the Transnational Public Sphere“, June 2022
Computational Social Science, Network Science, Machine Learning, Stochastic Processes, Software Studies
Dissertation: “Dynamical Network Analysis and Machine Learning for Computational Social Sciences”
D. J. Schindler and M. Fuller, ‘Community as a Vague Operator: Epistemological Questions for a Critical Heuristics of Community Detection Algorithms’. arXiv, May 24, 2023. doi: 10.48550/arXiv.2210.02753.
D. J. Schindler and M. Barahona, ‘Persistent Homology of the Multiscale Clustering Filtration’. arXiv, May 07, 2023. doi: 10.48550/arXiv.2305.04281.
A. Arnaudon et al., ‘PyGenStability: Multiscale community detection with generalized Markov Stability’. arXiv, Mar. 08, 2023. doi: 10.48550/arXiv.2303.05385.
D. J. Schindler, J. Clarke, and M. Barahona, ‘Multiscale mobility patterns and the restriction of human movement’. arXiv, Jan. 23, 2023. doi: 10.48550/arXiv.2201.06323.