Photo: Erik Thor/SUA
About 300 million people in Africa live in extreme poverty. Given that living in impoverished communities can trap people in cycles of deprivation (‘poverty traps’), major development actors such as China and the World Bank have deployed a stream of projects to break these cycles (‘poverty targeting’). However, as scholars are held back by a data challenge, research has up until now been unable to answer fundamental questions such as whether poverty traps exist, and to evaluate what extent interventions can release communities from such traps.
I am leading the AI and Global Development Lab to identify to what extent African communities are trapped in poverty and examine how competing development programs can alter these communities’ prospects to free themselves from deprivation. Our Lab has the following objectives: (i) train image recognition algorithms—a form of AI—to identify local poverty from satellite images, 1984-2020; (ii) use these data to analyze how development actors affect African communities; (iii) use mixed methods to develop theories of the varieties of poverty traps; (iv), develop an R package, PovertyMachine, that will produce poverty estimates from new satellite images—ensuring that our innovations will benefit poverty research.
Born: 10 November 1981
Interests: Family and walks are central parts of my day. I love trying new dishes from around the world as an inspiration for my own cooking. I never say no to an invitation to enjoy classic or new science fiction movies, or board games.
Other: Have played football in the junior Allsvenskan league.
I commit to the Young Academy of Sweden because the academy offers a unique opportunity to change, improve, and refine Swedish universities and their position globally.
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