Network Science

Members

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  Albert-Lászlo Barabási

  Professor Barabási’s research focuses on biological networks, science of success, applications of control theory to networks, and the development of network models of resiliency in systems.

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  Dima Krioukov

  Professor Krioukov’s expertise is in latent network geometry, maximum-entropy random graph ensembles and random geometric graphs, causal sets, navigation in networks, and fundamentals of network dynamics.

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  Esteban Moro Egido

  Professor Moro’s expertise is in big data, network science, and computational social science, with a focus on human dynamics, collective intelligence, social networks, and urban mobility in areas such as viral marketing, natural disaster management, and economic segregation in cities.

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  Mauricio Santillana

  Research in Professor Santillana’s group focuses on modeling of geographic patterns of population growth, modeling fluid flow to inform coastal floods simulations and atmospheric global pollution transport models, design and implementation of disease outbreak prediction platforms, and mathematical solutions to healthcare.

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  Alessandro Vespignani

  Professor Vespignani’s expertise includes contagion models and adaptive behavior, epidemics in structured populations, resilience of coevolving and interdependent networks, conversations in online social networks, global epidemic and mobility model, and mapping world languages through microblogging platforms.

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  Matteo Chinazzi

  Research Associate Professor Chinazzi conducts research at the intersection between network science, data science, epidemiology, economics, and artificial intelligence. His research interests include: a) the development of computational and analytical models to study and forecast the spatial spread of infectious diseases; b) the development of agent-based models to create realistic representations of population dynamics; c) the study of human mobility and contact patterns using high-resolution large-scale de-identified location data; d) the development of computational frameworks that combine mechanistic epidemic models with machine learning/deep learning models; and e) the study of the evolution and structure of science and innovation.

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  Brennan Klein

  Assistant Professor Klein’s research spans two broad topics: 1) Information, emergence, and inference in complex systems — developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Public health and public safety — creating and analyzing large scale datasets that reveal inequalities in the United States, from epidemics to mass incarceration