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umbertomig/README.md

Umberto Mignozzetti (UCSD)

Hi there 👋!

I am a Computational Political Scientist teaching at the Department of Political Science and the Computational Social Sciences Program at UC San Diego.

My methodological research uses deep learning to optimally combine tabular (data frames) and non-tabular data (images, video, audio, GIS data) sources. My substantive research focuses on improving public goods provision in developing democracies.

My papers have contributed to understanding the nexus between legislature size and welfare, failures in bottom-up accountability, the effects of elite capturing, and elite preferences toward climate change mitigation agreements.

My research has been published or is forthcoming in the American Journal of Political Science, British Journal of Political Science, Journal of Experimental Political Science, Research and Politics, and Global Environmental Politics.

My lab DeepVerse and my webpage have more details about my current and past work.

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  1. legislators-bargaining-costs legislators-bargaining-costs Public

    TeX

  2. wnomBrLeg wnomBrLeg Public

    Extract votes and compute W-Nominate scores Brazilian congress

    R 2

  3. POLI175public POLI175public Public

    Machine Learning for Political Scientists

    Jupyter Notebook 8 6

  4. CSSBootCamp CSSBootCamp Public

    Jupyter Notebook 3