Enacted legislation network from the 105th-112th Congress
An enacted legislation network for the 110th Congress. Legislative "importance" is measured as the pagerank centrality of each node. "Measuring and Modeling Legislative Accomplishment with Directed Graphs" (with Anthony Madonna).

Research Interests

  • Political economy.

  • Political institutions.

  • Social choice.

  • Machine learning.

  • Causal inference.


Welcome! For the 2017-2018 academic year I am visiting Princeton University as the Microsoft Visiting Assistant Professor at the Center for Information Technology Policy. I am an Assistant Professor in the departments of Public Administration and Policy and Political Science at the University of Georgia. I am also a faculty affiliate at the Institute for Artificial Intelligence. I teach methods courses in applied machine learning, causal inference, research design and quantitative methods.


% of California county budgets devoted to planning functions in 2016 using budget statements.
% of California county budgets devoted to "planning" functions (Schick 1966). From "Computational text analysis for public management research" (with Tima Moldogaziev and Tyler Scott)

My  substantive research focuses on political economy, political institutions and democratic accountability in the United States and Europe. My main project studies how legislative accomplishment in the United States is shaped by conflict between interest groups and constituency pressures.  The first portion of this project involves a conceptual and methodological re-assessment of how legislative accomplishment is understood using network analysis and machine learning methods for text analysis.

Other projects focus on developing an understanding of the nature of delegation of powers from a comparative political economy perspective.  A recent project with Anthony Bertelli, for example, explores how, when and why the European Union delegates powers to member states across time using formal models, text analysis methods and thousands of coded English and French language pieces of EU legislation.

My methods research interests include machine learning methods for text and image analysis with a special interest Bayesian inference for causal inference, deep learning, network analysis and the emerging field of algorithmic game theory.

My main projects in political methodology include providing epistemological and substantive justifications for causal inference from a Bayesian perspective and the development of causal inference methodologies for time series data using deep learning methods such as Long Short Term Memory (LSTMs) neural networks and General Adversarial Networks (GANs).