The National Science Foundation has awarded a Graduate Research Fellowship to Nina Kerkebane for the algorithmic mechanism she created to improve the refugee resettlement process.
The fellowship, which recognizes and supports graduate students in science, technology, engineering, mathematics, and social science Master’s and PhD programs, granted a three-year stipend to fund Kerkebane’s PhD research at the Harris School of Public Policy—work she began as a Graduate Student-at-Large (GSAL).
“I took two classes at Harris through GSAL, and both were instrumental in formulating a central question when I was applying to graduate school,” Kerkebane says. The first introduced her to conflict studies, a subfield of economics, while the second exposed her to fresh areas of economic modeling.
GSAL gave me access to professors whom I was able to speak with about my project. Talking out a project with specialists is important and can help you clarify your research and write your applications.Nina Kerkebane, GSAL Recipient
“There was a whole new world of models that I didn’t know about,” she says. “I used those ideas in my National Science Foundation proposal, and they will also be significant to my dissertation.”
In addition to inspiring her award-winning idea, GSAL provided essential support in other key areas, Kerkebane says.
“GSAL gave me access to professors whom I was able to speak with about my project. Talking out a project with specialists is important and can help you clarify your research and write your applications.” Further, GSAL granted her access to UChicago’s fellowship advising services that provided an expert review of her application materials.
Just as important, Kerkebane says, was the sense of community GSAL provided. When she started the program, she had just moved to Chicago in the middle of a pandemic. Through GSAL, she was able to find comrades in arms.
“I met a lot of people I could share ideas with and talk about all kinds of econ concepts,” she says. “But also, going through the experience of applications can be extremely stressful, and it was really helpful to know people going through the same thing.”
Improving Refugee Allocation
In her research, Kerkebane seeks to improve one of the most challenging aspects of refugee allocation: coordinating refugees with host countries in a timely fashion.
Kerkerbane has high hopes for her contribution, and a personal stake in its success. “I have seen the algorithm I proposed work well on my current project, and it is my hope that it will have a similarly positive impact on refugees,” she says. “I’m an asylee myself, and I really wanted to contribute to that area because allocation is something that I really care about.”
After her family faced persecution, Kerkebane, then twenty-two, fled her native Algeria for the United States. Using a tourist visa, she reunited with the family who had hosted her as a high school exchange student, now supporting her as she began the lengthy process of applying for political asylum.
In the three years it took Kerkebane to receive asylum, she considered ways to improve refugee allocation. This is currently a manual process, and as such, it is expensive, time-consuming, and subject to bias. What’s more, “the current approach doesn’t take the preferences of the refugees or the communities that they’re allocated into consideration,” Kerkebane says.
In her proposal for the National Science Foundation, Kerkebane outlined an alternative approach that uses algorithms to allocate and match refugees around the world.
“The approach I’m proposing will be able to carry out the matches more effectively and a lot faster, which means people’s time in conflict will be reduced,” she explains. “The magnitude of today’s refugee crisis—26.4 million persons and growing—means that even small efficiency gains have large benefits, especially for the world’s most disadvantaged.”