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AI Hub

We envision the AI hub to become a state-of-the-art knowledge platform on Trustworthy AI for the four alliance partners as well as third parties with established collaborations and consortia within the societal impact areas (Circular Society & Preventive Health).

Data science and AI are key enablers for scientific discovery through new technologies that allow for addressing new research questions that cannot be answered by hard-coded programming rules.

AI is not only a tool, but also a research field in its own right, when developing new insights or algorithms to work with data. However, the intrinsic risk of working with data is misuse, validity concerns or other unwanted effects. The alliance Artificial Intelligence program aims to stimulate research collaboration in the alliance leading to scientific discovery and societal impact while maintaining trustworthiness of AI.

In order to strengthen the implementation of trustworthy and explainable AI as enabling technology for preventive health and circular society, we will facilitate partnerships with the two respective institutes of EWUU – the Institute for Preventive Health and the Institute for Circular Society – to make sure that AI is applied in these domains in alignment with their research goals.

The objectives of the AI hub are to:

  • Perform research to foster scientific discovery through trustworthy AI.
  • Establish insights where AI can support and contribute to breakthrough developments within the alliance.
  • Connect the projects in the hub with the existing AI Labs of the alliance partners.
  • Collaborate with Preventive Health and Circular Society to (co-)fund projects in the two societal impact domains.
  • Scout national (and if possible, international) funding opportunities and involve researchers in consortium building events; capitalize on the emerging research collaborations within the alliance.

Our research lines:

AI-hub projects:

  • Empowering scientific discovery through AI
    Team: Rens van de Schoot (UU), Daniel Oberski (UMCU), Ricardo Torres (WUR), Virag Sihag, Chris Knighting (TU/e)
  • Dissemination of remote patient monitoring and wearables in the context of the “juiste zorg op de juiste plek”
    Team: Teus Kappen, Martine Breteler (UMCU), Pieter van Gorp (TU/e)
  • The personalization of medicine by machine learning for image interpretation
    Team: Nico van den Berg (UMCU), Mitko Veta, Josien Pluim (TU/e)
  • Algorithms for explainable AI
    Team: Maarten van Smeden (UMCU), Anna Vilanova Bartoli (TU/e)
  • ELSA lab proposal
    Team: Karin Jongsma (UMCU)