About me
Modern medicine has made incredible progress in the last 100 years. The development of technology to support medical personnel has contributed to this in particular. It is my wish to do research on data-driven medical technology of tomorrow and to support modern medicine with my contribution.
I am a research scientist at Philips Research in Hamburg working on machine learning algorithms for clinical applications in CT and MRI. Before that, I was a postdoctoral researcher at the Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology and my main research interest was Bayesian inference for uncertainty-aware medical image analysis with deep learning. During my PhD studies at Leibniz University Hannover, I have been working on calibration methods to obtain well-calibrated uncertainties from deep Bayesian models in medical imaging applications. I have experience in working with many modalities, including X-ray, CT, MRI, ultrasound, OCT, and (stereo) endoscopy.
Experiences
AI in clinical applications.
Deep learning and robotics in medical imaging applications.
Medical Technology and Image Processing
- Thesis title Well-Calibrated Predictive Uncertainty in Medical Imaging with Deep Learning
Mechanical engineering with focus on robotics and medical image processing
- Final grade 1.2 (with honours, GPA equiv. 3.8/4.0)
- Master’s thesis Three-dimensional tracking of soft tissue deformations for incision planning in lasery surgery
Mechanical engineering
- Bachelor’s thesis Registration of an operating table using the kinect sensor system