Dr. Sarah Brüningk

Dr.  Sarah Brüningk

Dr. Sarah Brüningk

Lecturer at the Department of Biosystems Science and Engineering

ETH Zürich

Biomedizinische Datenwiss.

LGA D 11

Lengghalde 2

8008 Zürich

Switzerland

Additional information

Research area

My research focuses on the interface of biology, medicine, mathematical modeling, and data science to provide personalized predictions for healthcare applications based on a variety of input data types. I have a particular interest in data-driven models for rare conditions where data is limited but the impact a prediction model could make in clinical application is particularly high. Specifically, I investigate personalized therapy regimes for pediatric brain tumor patients, including low and high-grade glioma with an emphasis on pediatric diffuse midline glioma. By using tailored input data types and a combination with mechanistic mathematical modeling I devise prediction pipelines that aim for an application as in silicon trial simulation platforms. In this context I investigate generative AI models for anatomical predictions of tumor growth on magnetic resonance imaging, radiotherapy response prediction based on imaging and multi-omics inputs, as well as biomarker identification from histology and magnetic resonance data. Apart from pediatric oncology, I am also involved in data-driven predictions to describe recovery from traumatic spinal cord injury and to detect critical conditions in pediatric intensive care units, such as sepsis. 

I am a physicist by training and obtained a BSc. in Physics and MSc. in Physics (Biophysics) from the Technische Universität München (Munich, Germany) in 2011 and 2014 respectively. After working for a few months as a medical physicist in the RadiationOncology Department at the Klinikum rechts der Isar (Technische Universität München University Hospital), I moved to the Institute of Cancer Research (London, UK) to pursue a PhD. Here, I obtained a PhD in Biophysics in 2019 on the topic of "Analysis and simulation of combination treatments of radiation and focused ultrasound mediated heating". Following a short stay at the ICR as a postdoctoral research fellow I moved to ETH Zurich (Switzerland) in September 2019 where I joined the Computational Biology lab of Prof. M. Claassen at the Institute of Molecular Systems Biology. In April 2020 I transferred to the Machine Learning and Computational Biology Lab (MLCB) of Prof. Karsten Borgwards at the Department of Biosystems Science and Egineering). Here, I started my Basel Research Centre for Child Health Postdoctoral Excellence Fellowship in October 2021. Due to the move of the MLCB lab, I transferred in February 2023 to the Biomedical Data Science lab (BMDS) of Prof. Catherine Jutzeler at the Department of Health Sciences and Technology. Since August 2023, I am leading the "Pediatric Oncology Application" subgroup with this lab. 

CV PDF

Course Catalogue

Autumn Semester 2024

Number Unit
376-1725-00L Introduction to Python Programming

MLCB publications

Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

Sarah C. Brüningk*, Juliane Klatt*, Madlen Stange*, Alfredo Mari, Myrta Brunner, Tim-Christoph Roloff, Helena M. B. Seth-Smith, Michael Schweitzer, Karoline Leuzinger, Kirstine K. Søgaard, Diana Albertos Torres, Alexander Gensch, Ann-Kathrin Schlotterbeck, Christian H. Nickel, Nicole Ritz, Ulrich Heininger, Julia Bielicki, Katharina Rentsch, Simon Fuchs, Roland Bingisser, Martin Siegemund, Hans Pargger, Diana Ciardo, Olivier Dubuis, Andreas Buser, Sarah Tschudin-Sutter, Manuel Battegay, Rita Schneider-Sliwa, Karsten M. Borgwardt°, Hans H. Hirsch°, and Adrian Egli° (* = equal contribution, ° = joint supervision)
Virus Evolution 2022, 8 (1): veac002
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