Label scarcity is a central challenge for machine learning in general and for medical application in particular. We explore ways to alleviate the dependence on labeled training data such as self-supervised learning, incorporating symmetries/side-information and meta learning.
Keywords: self-supervised learning, incorporation of symmetries/side-information, meta learning
Modern deep learning models are often perceived as opaque black boxes, which is a central barrier for bringing such tools into clinical applications. We explore interpretability methods, with particular focus on feature interactions and concept-based explanations. We try to measure and improve the robustness as well as other quality criteria for machine learning systems to increase the trustworthiness of machine learning systems.
Keywords: Explainable AI (XAI), interpretability, quality criteria, robustness, AI auditing
We aim to transfer state-of-the-art machine learning methods into the (bio)medical application space. Our main focus lies on physiological time series, in particular ECG data, but extends for example to ICU time series and beyond. In the biomedical domain, we are interested in inferring protein properties from its primary sequence. We are very open to other application domains within the broad domain of health data -- please reach out if your are interested in a collaboration.
Keywords: physiological time series, electrocardiography (ECG), intensive care unit (ICU), protein function