Theories, methods and algorithms developed by Integreat are of generic nature. However, a premise for research in knowledge-driven machine learning (ML) is the availability of real-world data and use cases to motivate, test and demonstrate the benefits of our methods. As a consequence, our research will also impact other scientific areas and society, because Integreat answers questions that currently cannot even be asked, as the investigative tools are missing.
Integreat’s methods will be applied and tested on real-world problems posed by a group of academic, industrial and societal partners. We have been working with many of them in our very successful centres for research-based innovation BigInsight, Sirius and Visual Intelligence, where we have reached a deep understanding of their specific domains. To extend our collaborations further, Integreat will establish a partnership programme that will give us a continuous stream of problems and data. We have access to a wide range of scientific data also through the dScience UiO centre.
We will select sectors that are mature for ML adoption, including high-impact applications like personalised medicine, computational social sciences, green energy transformation, infectious diseases modelling, foreign aid, electronic health records and real-world evidence.