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Spotlight on Research with Adel Daoud

​​​Observatory of Poverty: combining satellite images and deep-learning algorithms to estimate health and living conditions in Africa
About 900 million people globally—one-third in Africa and another one-third in India—live in extreme poverty. Operating on the assumption that impoverished communities are trapped in poverty, major global donors have deployed a stream of development programs to break these traps. Despite the scale of programs, scholars have little knowledge about the distribution of global poverty historically and geographically. To address these knowledge gaps, scholars must first tackle a data challenge: the lack of historical and geographical poverty data. 

The AI and Global Development Lab is innovating development research by combining deep-learning, satellite technologies, and knowledge on human development to overcome the data challenge. The Lab is recreating historical and geographical human-development trajectories from satellite images from 1984 to 2022. These new data will measure poverty at unprecedented temporal and spatial granularity. Among other things, these data will enable the Lab (and other scholars) to start examining—with a high precision—the causal effects of foreign aid on poor communities’ chances of breaking poverty. This talk will discuss key scientific challenges and early findings.

Register for the seminar here to receive the Zoom-link. 

Adel Daoud is an Associate Professor at Institute for Analytical Sociology, Linköping University, and Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences, Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden. Previously he held positions at Harvard University, University of Cambridge, Max Planck Institute for the Studies of Societies, and the Alan Turing Institute. His researh has both a social-scientific and methodologically orientation. For the social sciences, he researchers the effect of international development interventions (e.g., anti-poverty policies) on global poverty, but also the impact of sudden shocks (e.g., economic, political, and natural disasters). Daoud implements novel methodologies in machine learning and causal inference to analyze the causes and consequences of poverty. He has published in journals such as PNAS, Science Advances, World Development, International J of Epidemiology, and Ecological Economics, and machine-learning conferences as AAAI. 

Daoud leads The AI and Global Development Lab (more information at The vision of the Lab is to “combine AI and earth observation to estimate sustainable and human development globally.” The Lab is mainly located at DSAI and IAS. At DSAI, Dr. Fredrik Johansson and Professor Devdatt Dubhashi are co-PIs. The Swedish Research Council funds this Lab through a Research Environment Program and a Consolidator Grant. Pilot funding and IMCG support comes from Chalmers AI Research Centre (CHAIR). Google, in partnership with the Group on Earth Observations, provides mentorship and in-kind technical support for the Lab. Key partners of the Lab are Dept of Political Science, University of Gothenburg and the Department of Statistics, Harvard University. 

More information is provided at​
Category Seminar
Location: Zoom. Register above to receive the link.
Starts: 25 February, 2022, 14:00
Ends: 25 February, 2022, 15:00

Page manager Published: Fri 18 Feb 2022.