AI for Healthcare

Head of the Artificial intelligence Research Group

We are devising and applying artificial intelligence (AI) techniques to solve problems within the field of biomedicine. We are developing and implementing machine learning algorithms, statistical methods and mathematical models to analyse biomedical data ranging from electronic health records to cancer genomic data. We closely collaborate with researchers, clinicians, and industry experts in biology and medicine. We want to contribute to a better understanding of biological complexity and of medical decision-making.

News

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Research

Currently, there are two major research directions:

Machine Learning (ML-) Methods for learning causal dynamic models from data

The aim is to use longitudinal data to learn the dynamics of a biomedical process (e.g. disease courses or pharmaodynamics) from data and to simulate the effect of interventions (e.g. treatments or experiments in biology). Here we combine techniques from ML with methods from control theory and statistics. One current application is to use ML to automatically improve systems biology models and another is to predict time dependent treatment effects using counterfactual inference in dynamic systems.

High dimensional data analysis and modelling in cancer biology