About me

I am leading the independent research group for Machine Learning in Medical Image Analysis at the University of Tübingen as part of the Cluster of Excellence - Machine Learning for Science.

Research Areas

My research focuses on developing machine learning methodologies that bridge the gap between ML theory and clinical applications. I am pursuing this goal along those broad directions:

  • Safety and uncertainty: In medical image analysis, confidently predicting something false can have devastating consequences. Hence, it is crucial to develop machine learning algorithms that reflect the various uncertainties in the medical image analysis pipeline and can help clinical practitioners to safely use this technology in practice.

  • Learning efficiently with fewer data: Obtaining annotated data is very expensive in the medical field because only clinical professionals can do it. How can we learn with fewer data, and how can we obtain training data that are optimal for a certain task?

  • Exploiting shared information between tasks: Often algorithms are learned from scratch for each new problem. Taking into account that many problems are related in various ways can help us create more intelligent algorithms.

  • Discovering effects in big medical data: Recent advances in probabilistic machine learning techniques offer a unique opportunity to explore datasets with ten thousands of images (such as the German National Cohort Study) to better understand disease processes.

See more details in the Research Interests section.

Short Bio

Before joining the University of Tübingen, I was working in a senior research engineering role at PTC Vuforia, where I worked on research and develoment of machine learning technology for augmented reality applictaions. Prior to this, I was a Post-doc at the Biomedical Image Computing Group at ETH Zürich working with Prof. Ender Konukoglu, and before in the Biomedical Image Analysis Lab with Prof. Daniel Rueckert. I completed my PhD in 2016 under the joint supervision of Prof. Andy King and Prof. Daniel Rueckert at King’s College London in the School of Biomedical Engineering & Imaging Sciences (link to PhD thesis). I obtained my Master’s degree in Biomedical Engineering and my Bachelor’s degree in Information Technology and Electrical Engineering from ETH Zürich.


  • April 2021: We have two new openings for PhD students and a post-doc! See the jobs page for more details.
  • Feb 2021: Officially started my position at the University of Tübingen.
  • Oct 2020: Accepted position as Independent Research Group Leader at the University of Tübingen
  • Oct 2020: You can apply for PhD positions in our group through the International Max Planck Research School for Intelligent Systems (IMPRS-IS). The deadline is Nov 2, 2020. Get in touch for details how to apply to work with us! More details here.
  • Oct 2020: Accepted position as Independent Research Group Leader at the University of Tübingen