About Me

I am a group leader in the Berens Lab at the University of Tübingen. I work on machine learning for medical image analysis and have a background in academic research as well as developing machine learning products for medical devices. I have a strong focus on research that can ultimately benefit patient health in a safe and secure way.

Bio

After an undergraduate degree in electrical (BSc) and biomedical engineering (MSc) at ETH Zürich, Switzerland, I did a PhD in machine learning for medical image analysis at Imperial College London under the supervision of Prof. Daniel Rueckert. After a post-doc with Prof. Marc Pollefeys at ETH Zürich I joined the Swiss medical device startup Ava, where I eventually became the Machine Learning team lead. In this position, I came to appreciate the need for demonstrably safe machine learning in healthcare.

Research interests

My research focuses on several aspects of machine learning for medical image analysis, including:

  • Domain shift detection
  • Uncertainty quantification
  • Neural network calibration
  • Performance generalisation and performance prediction

Teaching

In the winter semester 2021/2022, I am teaching the seminar Machine Learning for Medical Image Analysis

News

  • December 2021: I was awarded the Athene Grant by the University of Tübingen
  • November 2021: The Carl Zeiss Foundation is funding our project “Certification and Foundations of Safe Machine Learning Systems in Healthcare”, led by principal PI Prof. Matthias Hein. Very proud to be part of this excellent group of researchers, and looking forward to the research coming up.
  • September 2021: I am teaching the seminar Machine Learning for Medical Image Analysis in the upcoming semester. Sign up now and join us!
  • March 2021: I’m looking for motivated students for MSc thesis projects. Please get in touch to find out about potential topics.
  • March 2021: I joined the Berens Lab as a group leader for machine learning in medical imaging after a few years of industry experience.