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Bruce Desmarais
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Governance and Administration in Networks (GAiN) Lab

The GAiN Lab, directed by Bruce Desmarais, conducts research focused on rigorously and precisely identifying the complex ways in which political actors and institutions exhibit interdependence. Approximately half of the Lab's work involves the development of analytical methods capable of illuminating these dependencies. The other half involves applications in the study of interdependence in law and policymaking. Below I provide summaries of our active projects. The Lab's funding is provided by the National Science Foundation, the Russel Sage Foundation and the William and Flora Hewlett Foundation. My lab has a site on GitHub.

Lab Personnel

Graduate Researchers

Fridolin Linder

Fridolin Linder is a third year PhD student in political science. His research focuses on the application and development of text analysis and machine learning tools to problems in political science. Before coming to Penn State he studied at the University of Vienna, University of Mannheim and Washington University in St. Louis.


fridolin.linder [at] gmail [dot] com

Bomin Kim

Bomin Kim is a fourth year Ph.D student in statistics. Her research focuses on statistical methods for identifying the latent structure of dynamic networks, joint modeling of event history and textual contents, and application of these methods in social sciences. Before coming to Penn State, she has done her bachelors in Statistics from Korea University in Seoul, Korea.


bzk147 [at] psu [dot] edu

Sayali Phadke

Sayali Phadke is a third year PhD student in Statistics. She is interested in applications of statistics to social sciences. She has done her bachelors in Economics and Statistics from University of Mumbai, India, prior to taking post-graduate liberal arts training.


ssp5208 [at] psu [dot] edu

Christian Schmid

Christian is a PhD student in the Statistics department. He is interested in network models and their potential applications in social science. One methodological problem he is currently researching involves using network approaches to estimate the size of hidden population groups. He is also working on developing new estimation techniques for network models. Before joining the Penn State family he received his BS in Mathematics and his MS in Statistics at the Ludwig Maximilians University in Munich, Germany.


schmid [at] psu [dot] edu

Matthew J. Denny

Matt Denny is a Ph.D student in political science. His research focusses on developing new statistical models for text, networks, and text-valued networks. Matt holds Master’s degrees in Applied Econometrics and Political Science from the University of Massachusetts Amherst.


mdenny [at] psu [dot] edu

Markus Neumann

Markus Neumann is a PhD student in Political Science and Social Data Analytics. His research focuses on applying text-statistical methods and deep learning to social science data. Substantively, his interests include campaigns, elections and political rhetoric. Markus earned Bachelor and Master degrees in Political Science from the University of Mannheim, Germany.


mvn5218 [at] psu [dot] edu

Lab Projects

Organizational Responsiveness to Open Outside Input

Sponsor: National Science Foundation

Period: 2013--2016

This project focuses on the development of new analytical tools for modeling the relationships between intra-organizational communication networks and open, external sources of text data. The massive quantities of textual communications generated within organizations constitute a largely untapped source for insightful, timely organizational analytics. The tools under development for this project are designed to jointly analyze the content of communications and the socio-organizational structure comprised by communication ties.

Scientific Evidence in Regulation and Governance

Sponsor: National Science Foundation

Period: 2014--2017

Public policymaking is enhanced through access to the best possible science. Central to the project is the development of the first large-scale, publicly available database that connects specific policies to specific scientific sources, allowing comparisons across time, policymaking domains, and scientific disciplines. This database better illuminates the basis of regulatory impact assessments, reveals how science is presented to policymakers, and provides scientific researchers as well as their funders with concrete evidence of real-world policy impact.

Specification and Estimation of Generalized ERGMs

Sponsor: National Science Foundation

Period: 2014--2016

The generalized exponential random graph model (GERGM) is a powerful tool for formulating and testing hypotheses about weighted networks. This project advances the current state of development of the GERGM by (1) developing a better understanding of the space of distributions that can be formulated with the GERGM; developing MCMC methods for estimation, which will broaden the class of available GERGM specifications; (3) developing constraints that facilitate the study of correlation matrices as networks; and (4) developing asymptotic theory of GERGM.

The Revolving Door in Financial Regulation

Sponsor: Russell Sage Foundation

Period: 2014--2016

We model the revolving door as a social network and assess the variable levels of advocacy success of interest groups who lobby over financial regulation designed by the Securities and Exchange Commission (SEC). The project employs data from personnel flows, lobbying and regulatory policy documents, and then utilizes best practice techniques to measure advocacy success, to control for confounding variables and to maximize causal inference within a social network analytical frame.


230 Pond Lab, (814) 863-1595, bdesmarais'at'

Deptartment of Political Science | Institute for CyberScience | Penn State