Dr Sarah Brüningk is an emerging leader in computational approaches to radiation oncology and cancer treatment optimization. She currently serves as Assistant Professor at the University of Bern where she leads the Center for AI in Radiation Oncology at Inselspital as an SNSF Starting Grant Recipient. Previously she was a postdoctoral research fellow at ETH Zurich working within the Machine Learning and Computational Biology Laboratory and as a group leader for paediatric oncology applications within the Biomedical Data Science lab. Her academic foundation was established through a PhD at the Institute of Cancer Research in London where she analyzed and simulated combination treatments of radiation and focused ultrasound-mediated heating. Dr Brüningk's distinctive background in medical physics and computational biology uniquely positions her at the forefront of interdisciplinary cancer research.
Dr Brüningk possesses a rare dual expertise in practical biological laboratory work and advanced computational implementations enabling her to pursue complex projects at the interface of computational biology machine learning and oncology. Her research focuses on combining machine learning with mechanistic modeling for healthcare applications that embrace clinical disease hallmarks to provide interpretable and deployable healthcare solutions. This integrative approach has significantly advanced predictive modeling for cancer treatment response across multiple therapeutic modalities including immunotherapy combinations and radiation therapy. Her scholarly contributions have been recognized with distinguished awards including the Nadine Barrie Smith Student Award and the Sensius Young Investigator Award. With over 950 citations her work demonstrates substantial impact and growing influence within the computational oncology community.
As director of the Center for AI in Radiation Oncology Dr Brüningk is advancing innovative computational methodologies to transform radiation therapy planning and delivery. Her current research prioritizes the development of interpretable AI systems that clinicians can trust and integrate into routine medical practice through the application of deep learning and representation learning techniques. Through her SNSF Starting Grant she is expanding work on predictive models using multi-parametric data including liquid and solid biopsies radiological information and molecular profiling. Dr Brüningk actively fosters collaboration between computational scientists and clinicians to accelerate translational research outcomes. Her ongoing trajectory promises significant contributions to precision radiation oncology that will ultimately enhance patient care and treatment efficacy worldwide.
