Alexandre is the founder and head of the Quantitative Network Science group. His team develops machine learning methods and mathematical models to provide quantitative answers to social, biological, and economic questions.
Juni Schindler is a postdoctoral researcher in the Quantitative Network Science group at DM³L since November 2025. They contribute to the DIZH-funded project “New Digital Tools for Media Monitoring and Discourse Analysis”, which develops a machine learning framework grounded in communication theory to map how different journalistic perspectives interconnect in media reporting. More broadly, Juni draws on network science, machine learning, and topological data analysis to design methods for the multiscale analysis of complex networks, with applications in computational social science. Juni completed their PhD research under the supervision of Prof. Mauricio Barahona in the Department of Mathematics at Imperial College London, focusing on topological and graph-diffusion-based techniques for multiscale clustering. Prior to the PhD, they earned an MSc in Applied Mathematics from Imperial College and an MA in Digital Media from Goldsmiths, University of London.
Yasaman Asgari is a PhD student in Data Science at the Department of Mathematical Modeling and Machine Learning (DM3L) and Digital Society Initiative (DSI) at the University of Zurich since September 2023. Before her doctoral studies, Yasaman earned her Master’s degree in Computer Science from École Normale Supérieure de Lyon, France, where she explored building evaluation settings for testing dynamic community detection in fine-grained temporal networks. Her research interests include complex systems, temporal networks, and dynamic community detection with applications to real-world problems.
Samuel has been a member of the Quantitative Network Science group since August 2022. Before joining the Department of Mathematical Modeling and machine learning (DM3L) as a PhD student, he received a BSc and MSc in mathematics from ETH Zurich. During his Master’s thesis, he worked on a graphical model for predicting protein-protein interactions as an intern at the IBM Research Lab in Zurich. His main research interest is the study of stochastic processes and statistical properties of temporal networks, particularly the use of diffusion processes over graphs and tools from information theory to investigate the structural properties of these systems. More broadly, he is interested in topics at the intersection of probability theory and statistics, such as Markov processes, graphical models, machine learning, and causal inference.
Dorian is a PhD Candidate in the Quantitative Network Science Group (DM3L) at the University of Zurich, supervised by Prof. Alexandre Bovet. His research focuses on computational social science and network analysis, particularly investigating multilingual misinformation, polarisation, and algorithmic curation through the analysis of social media networks. His current research examines the spread of misinformation, network topology, and social media dynamics. He works to develop computational methods to understand and combat the spread of misinformation across linguistic and platform boundaries. Alongside his academic work, Dorian is a Data Scientist at Pattrn.AI. He holds an MSc in Social Data Science with Distinction from the Oxford Internet Institute, where he currently serves as a Visiting Researcher under the supervision of Scott Hale.
Yuan is a Ph.D. student in the Department of Communication and Media Research (IKMZ) at the University of Zurich, co-supervised by Alexandre Bovet at the DM3L. She holds Master’s degrees in Social Data Science from the Oxford Internet Institute and Political Economy and Political Science from the London School of Economics. She is a visiting scholar at the Annenberg School for Communication, affiliated with the Center for Information Networks and Democracy (CIND). She examines the complexity of individuals’ political selective exposure and investigates the mechanisms driving the spread of incivility on social media using computational methods, including natural language processing (NLP), large language models (LLMs), and complex network analysis. Her work aims to integrate the analytical power of computational and network modeling with the interpretive depth of communication theory.
