Marcos Oliveira is an Assistant Professor in Computer Science at the Department of Computer Science of the Vrije Universiteit Amsterdam, where he is a member of the AI & Behaviour group. He is also affiliated as an Honorary Lecturer with the Department of Computer Science at the University of Exeter in the United Kingdom. Previously, he was a postdoctoral researcher at the GESIS–Leibniz Institute for the Social Sciences in Germany.

His research lies at the intersection of computational social science, complex systems, network science, and data science, and is strongly informed by concepts from sociology and criminology. He studies how societal challenges such as social inequality, urban crime, and social risks of digitalization arise from individual behavior and interaction. His work combines large-scale behavioral data with mathematical and computational models to uncover mechanisms that drive social structure and dynamics. Empirically, he works with data ranging from online browsing activity to sensor-based measurements of face-to-face interaction, with applications to topics such as privacy, predictability, network inequality, and crime dynamics.

Marcos Oliveira has published in a wide range of international journals, including Nature Communications Physics, Nature Scientific Reports, Crime Science, Royal Society Open Science, and PLOS One. His research has been supported by competitive grants, including UKRI/Innovate UK and Facebook Integrity & Impact Research. In addition to his research activities, he serves on the Editorial Board of PLOS Complex Systems and has held various academic service and leadership roles.

Marcos Oliveira's research asks how individual actions and interactions give rise to large-scale social patterns and challenges, and how explicit computational models can be used to understand the mechanisms underlying these processes. Central questions include: How do social groups form and evolve? Under what conditions do interaction patterns lead to social isolation or inequality? How predictable is human behavior in digital and physical environments, and what are the limits of such predictability? How does individual uniqueness emerge from behavioral data, and what does this imply for privacy, profiling, and urban crime?

To address these questions, he combines large-scale behavioral data (e.g., online browsing traces, sensor-based measurements) with theory-informed, interpretable computational models. His work bridges computer science with sociology and criminology, using network analysis and complex-systems approaches to study social dynamics in contexts ranging from online environments and social gatherings to cities.

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