Currently, many refugees and nations don’t know the most optimal migration paths. Therefore, refugees are placed in dangerous routes, risking their lives and progress. We use historical migration data in order to learn the most efficient paths for refugee migration.
Governments struggle placing refugees in correct job opportunities to maximize economic output and assist refugees. We use machine learning to predict the most effective job placements for refugees, helping governments assimilate refugees into communities more effectively.
Once refugees arrive in new nations, they are often in limbo at refugee camps and centers because of delays in processing their home requests. We use models to match refugees to specific communities based on needs, interest, and availability.
We use AI to address many challenges refugees and governments face in migration.
We use geographical data on historical migration patterns to learn efficient and effective paths for refugees to follow. We request data from governments to build personalized paths to certain locations and nations.
We process our data in a flask backend. The data is processed and then passed to our machine learning models, which are all partially hosted on the web backend, allowing for easy interpretation and access.
We use three separate machine learning algorithms to generate paths, match refugees to communities, and match refugees to jobs. This helps refugees get to new nations faster. Our models are inspired by state-of-the-art research. View our documentation page for more information.
This information is presented on an interactive UI for governments to interpret how to plan refugee migration. Government officials can then relay information to migration teams to assist refugees escape their previous nations.