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Statistical Network Science

In the interconnected world of the twenty-first century, networks are ubiquitous. Examples include networks of neurons used in neuroscience to study how the human brain operates; networks of artificial neurons used in artificial intelligence to construct intelligent machines; networks of interacting genes used in the life sciences to study genetic diseases; networks of contacts used to study how infectious diseases spread; networks of economic and financial transactions used in economics to study the economy; and social networks used in the social sciences to study how disinformation spreads through social media.

Penn State Statistics has one of the world’s leading research groups in statistical network science, composed of at least five faculty members: Dave Hunter, Maggie Niu, Michael Schweinberger, Lingzhou Xue and Yubai Yuan. Specific areas of research range from latent structure models that capture who is close to whom to models that capture local and global features of networks, and include computational and statistical methods along with research on the mathematical foundations of learning from data how the interconnected world of the twenty-first century operates.


Associate Professor, Director of the Statistical Consulting Center