Machine learning has the potential for tremendous health innovations, but applying it in healthcare poses novel and interesting challenges. Data privacy is paramount, applications require high confidence in model quality, and practitioners demand explainable and comprehensible models. Ultimately, practitioners and patients alike must be able to trust these methods. In our research group on Trustworthy Machine Learning we tackle these challenges, investigating novel approaches to privacy-preserving federated learning, the theoretical foundations of deep learning, and collaborative training of explainable models.
Open Positions
- We offer Master and Bachelor theses for students within the UA Ruhr
If you are interested, please send an email to Michael Kamp.
Latest News:
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Federated Daisy-Chaining
How can we learn high quality models when data is inherently distributed across sites and cannot be shared or pooled? In federated learning, the solution is to iteratively train models locally at each site and share these models with the server to be aggregated to a global model. As only models are shared, data usually…
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Nothing but Regrets – Federated Causal Discovery
Discovering causal relationships enables us to build more reliable, robust, and ultimately trustworthy models. It requires large amounts of observational data, though. In healthcare, for most diseases the amount of available data is large, but this data is scattered over thousands of hospitals worldwide. Since this data in most cases mustn’t be pooled for privacy…
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Causal Discovery from Multiple Environments
At AAAI 2023 my colleagues Osman Mian, Jilles Vreeken and me presented our paper “Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments” in which we learn a global structural causal model over multiple environments, as well as discover potential local intervention that change some causal relationships within particular environments. For medical data this has…