Kirk is a PhD candidate in political science at Stanford University. He also serves as a data scientist at the Immigration Policy Lab, a social science research lab focused on policy evaluation and design, with branches at Stanford and ETH Zurich. In addition to immigration policy, his primary research interests are in experimental design and analysis, computational social science, and international political economy. He holds an M.S. in statistics from Stanford, an M.A.L.D. from the Fletcher School, and a B.A. in anthropology from Harvard University.

Research Summary

The world is currently experiencing the worst refugee crisis since World War II. While refugees bring significant potential to contribute to their host communities, the challenges of resettlement present serious obstacles to realizing that potential. Furthermore, little empirical evidence exists on the efficacy of different approaches to integrating refugees within host countries. Currently, the United States and other major refugee-receiving countries assign refugees to locations based primarily on simple constraints such as local capacity, without fully exploring the insights that historical data can provide regarding which refugees do better in which locations.

To address this issue, Kirk’s research at the Immigration Policy Lab focuses on developing and testing a data-driven assignment algorithm that combines supervised machine learning and optimal matching procedures to assign cohorts of refugee arrivals across resettlement locations such that their employment prospects (or other integration outcomes) are maximized. The algorithm achieves this by identifying and leveraging synergies between refugee characteristics (e.g. age, gender, language skills) and resettlement locations. It will be tested via randomized controlled trials with partnering resettlement agencies.