I served on the jury of AI4GCC, a competition where teams designed negotiation agreements for climate change using a climate-economic simulation with AI agents.
I led the development of Agronomist AI, a context-aware chatbot that can translate a corpus of scientific papers on nitrogen modeling into actionable insights for farmers on the ground.
I have a working paper outlining the advantages of machine learning to study complex policy problems.
Complex policy problems like climate change and illicit finance require a diverse methodological repertoire and an agnostic approach to selecting the appropriate analytical tool to accomplish discrete inferential tasks. Drawing from the disciplines of political science, economics, and statistical data science, the project presented here tackles three distinct problems on causal evaluation, measurement, and missing data. This paper introduces the broad analytical lens of the dissertation and offers a perspective on empirical research for the study of complex real-world policy problems. The article argues that climate change and illicit finance can be understood as ``wicked problems'', and that doing so reveals the epistemological limitations of the common inferential framework that underlies much of the policy-relevant research in applied social sciences. Instead, this paper makes the case that machine learning approaches are uniquely suited to the study of “wicked” problems which resist systematic a priori formulation. The inferential framework of machine learning does not require us to accept that there is a simple generative model for the problem that can be known to be true. Instead, machine learning can be deployed in conjunction with domain knowledge to generate policy-relevant insights without requiring strong assumptions on the data-generating process in nature.
