Labs & Centers

The Center for Complex Network Research (CCNR) has a simple objective: think networks. The center’s research focuses on how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems, with applications ranging from the network of human diseases to controlling complex social, economical, and biological systems.

The Communication Media and Marginalization (CoMM) Lab aims to understand how and why marginalization happens by using communication power and media control theories, network and computational social science methods, and data from online and traditional media sources. From social media to businesses to schools and more, CoMM Lab aims to identify - and disrupt - the explicit and implicit processes that prevent some people and ideas from being fully heard.

The Collaborative Social Systems Lab explores how groups of individuals solve challenging tasks using local, distributed interactions. They use agent-based modeling, conduct lab and field experiments, and analyze large datasets to study how networked interactions influence human behavior, strategies, and success in multiple contexts.

The Complex Connections Lab is dedicated to studying the complex social connections that govern human behavior and interactions in cities and online. The lab focuses on the study of our digital traces left behind to capture human behavior and social dynamics through the lens of network science, complexity theory, and Data Science.

Work at the Complexity & Society Lab, or the "And Lab", is centered around theoretical and methodological advances in Complex Systems science, with focus areas in network dynamics, learning and inference, network generative models of AI architectures, and graph comparison.

DK-Lab research focuses mostly on network theory. Specific topics include network geometry, random (geometric) graphs, causal sets, navigation in networks, and fundamentals of network dynamics.

The IDEAS Lab strives to improve the human condition by advancing our understanding of socio-technical systems. The approach involves conducting use-inspired research at the intersection of systems biology and artificial intelligence. Specifically, they test hypotheses by constructing mathematical/computational models and evaluating them with data via the development and application of rigorous–often novel–machine learning and AI methods.

Lazer Lab research is based on the idea that how people and organizations are connected together is critical to understanding the functioning, success and failure of actors and systems and ranges from the very micro (social influence processes within groups), to the very macro (the development of global-wide regulatory regimes).

The Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) works on the the conception and implementation of machine learning analytics tools aimed at characterizing the dynamics of complex systems to anticipate and predict unobserved events in epidemiology, healthcare, and environmental sciences.

The Laboratory for the Modeling of Biological and Socio-technical Systems (MOBS) is home to research projects aiming at developing innovative mathematical models and computational tools to better understand large-scale complex networks and systems.

The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society. We do this with statistical mechanics, algebraic topology, snark, and cyberpunk.

Research in the Psychology of Misinformation Lab investigates what drives belief in inaccurate information, why certain individuals are predisposed to refrain from belief change even in the face of good corrective evidence, and how corrections can be designed to maximize impact.

Rad-Lab focuses on data mining and machine learning strategies to address challenges of networked representations of physical and social phenomena. Their research develops theory, algorithms, and applications.

The Social Urban Networks (SUN) Lab research lies in the intersection of big data and computational social science, with special attention to human dynamics, collective intelligence, social networks and urban mobility in problems like viral marketing, natural disaster management, or economical segregation in cities.