Applications:
Ecological Research, Information Systems, Management Science, Networks, Social Sciences
Methodologies:
Bayesian Methods, Causal Inference, Graph Theory and Graph-based Methods, Machine Learning, Network Analysis, Statistical Inference

Abigail Jacobs

Assistant Professor

Information, School of Information
Complex Systems, College of Literature, Science, and the Arts

I am interested in how governance, communities, and inequality emerge in sociotechnical systems, and how the structure of sociotechnical systems encodes and reinforces these processes. To those ends, I develop empirical data and computational methods, focusing on latent variable models; statistical inference in networks; empirical design to study governance in organizations, platforms, and computational social systems; and causal inference and measurement in observational data.

Several sample projects:
> developing empirical populations of networks to infer social and ecological processes encoded in networks
> using probabilistic methods to infer the structure and dynamics of the illicit wildlife trade
> building from theory from political science, statistics, and education to disentangle issues of “bias” in computational systems