Anastasopoulos has done extensive applied and theoretical work in machine learning and causal inference. His applied work has focused on developing machine learning techniques for research on political institutions, as well as developing deep learning techniques to measure political ideology in images. His theoretical work has focused on developing machine learning and computational techniques for causal inference and econometric applications.
Anastasopoulos has also worked as a machine learning scientist in the Python and R languages for governments and research organizations and has held academic positions at Princeton University, Harvard University and the University of California, Berkeley. He holds a doctorate in political science from the University of California, Berkeley, and a master’s degree in statistics from Harvard University. His work has been published in the American Political Science Review, Political Analysis and the Journal of Public Administration Research and Theory.
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