Research
I work at the intersection of political methodology and international relations, using diverse methods and micro-level data to study how the international community coordinates and collaborates to support the most fragile states in the international system. Empirically, I use and develop novel methodological tools to address sparseness and inconsistencies in subnational administrative and remote sensing data, a perennial problem in the cross-national study of fragile states that, by definition, often have limited reliable government data. These approaches include machine learning, generative AI, and other computational methods as well as formal theory and field work.
You can find my paper "A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data" from AIStats (with Yehu Chen, Jacob Montgomery, and Roman Garnett). We have a working paper version for the social sciences.
Here are my other working papers:
Building State Capacity Locally: International Interventions, Delegation, and Local Governance in Fragile States (Job Market paper)
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Why not both? Combining human and LLM labels in event data
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Beyond Peacekeeping: The United Nations Development Programme, Peacebuilding, and Violence Mitigation (Awarded Best Poster from APSA at the 2023 conference; Under review)
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A Gaussian Process Framework for Structured, Flexible, and Interpretable Machine-Learning Models (with Yehu Chen, Jacob Montgomery, and Roman Garnett)
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Border Fortifications and Trust in Government (with David Carter, Beth Simmons, and Michael Kenwick)
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