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Resettlement that empowers refugees and communities

Refugees.AI

 

Our goal

Refugees.AI takes the hopes, aspirations and goals of refugees seriously.

 

We believe refugee resettlement works best when we take into account the outcomes and preferences of refugees as well as the priorities and capacities of communities that host them.

 

We are using tools from machine learning, integer optimization, and matching theory to find the best matches between refugees and local communities.

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An official report by the Swedish Government and the Independent Chief Inspector of Borders and Immigration in the UK have recommended improving the geographic matching of refugees.

Introducing Annie™ MOORE 

Annie™ MOORE (Matching and Outcome Optimization for Refugee Empowerment) is the world's first software that helps resettlement agencies optimize their initial placement of refugees within host countries. It is named in honor of Annie Moore, the first person to be processed at Ellis Island in 1892.

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Read about how Annie™ has been helping a US resettlement agency since May 2018 here.

 

Annie™ MOORE: Improving refugee resettlement

Annie™ MOORE: Improving refugee resettlement

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Media coverage

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If you have a media enquiry, please contact:

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US: Alison Duffy (amduffy@wpi.edu)

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UK: Lanisha Butterfield (lanisha.butterfield@admin.ox.ac.uk)

 

Sweden: Louise Larsson (louise.larsson@ehl.lu.se)

 

By the Numbers

68.5 million

Forcibly displaced people in 2018

19.9 million

Refugees under UNHCR mandate in 2018

1.19 million

Refugees were in need of resettlement in 2017

75,188

Refugees submitted for resettlement in 2017

65,109

Refugees, who fled from 70 countries, departed for resettlement in 30 countries in 2017

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despite increases in departures, the total annual number of resettlement country places...were not fully utilized.

UNHCR Refugee Resettlement Trends, 2015, p.28

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Resources

Selected Articles and Blogs
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Selected Academic Work

  1. Trapp, A. C., Teytelboym, A., Martinello, A., Andersson, T. and N. Ahani (2018), "Placement Optimization for Refugee Resettlement", Working paper.

  2. Bansak et al. (2018), "Improving refugee integration through data-driven algorithmic assignment", Science.

  3. Andersson, T., Ehlers, L., and A. Martinello (2018), "Dynamic Refugee Matching", Working paper.

  4. Jones, W. and A. Teytelboym (2018), “The local refugee match: Aligning refugees' preferences with the capacities and priorities of localities”, Journal of Refugee Studies.

  5. Aziz, H. et al. (2018), Stability and Pareto optimality in refugee allocation matchings, AAMAS '18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

  6. Grech, P. (2017), "Undesired properties of the European Commission’s refugee distribution key", European Union Politics

  7. van Basshuysen, P. (2017), "Towards a fair distribution mechanism for asylum", Games.

  8. Jones, W. and A. Teytelboym (2017), “The international refugee match: A system that respects refugees' preferences and the priorities of states", Refugee Survey Quarterly.

  9. Andersson, T. and L. Ehlers (2016), “Assigning refugees to landlords in Sweden: Efficient stable maximum matchings", Working paper.

  10. Delacretaz, D., Kominers, S. D., and A. Teytelboym (2016), “Refugee Resettlement”, Working paper

  11. Jones, W. and A. Teytelboym (2016), “Choices, preferences and priorities in a matching system for refugees”, Forced Migration Review.

  12. Moraga, J. F.-H. and H. Rapoport (2014), “Tradable Immigration Quotas”, Journal of Public Economics.

About Us

Researchers in the UK

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Researchers in the US

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Researchers in Sweden

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Contact Us

If you would like to work with us on designing matching systems for refugee resettlement, please drop us a line.

generously supported by

© 2019 Refugees.AI

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