Additionally, I am a student at the Center for Digital Technology and Management in the class of Spring 2020, pursuing an honours degree in Technology Management.
Next to my studies, I work at Capmo, a Munich-based startup digitalising the construction industry. My initial focus was on data science and automation but transitioned more into a backend software development role to further extend the platform.
Areas of interest include machine learning for medical applications, safe and interpretable deep learning and applications of AI.
During and inbetween my studies I worked at the Computational Imaging and Bioinformatics Lab (CIBL) at Harvard Medical School and Dana-Farber Cancer Institute under Prof. Aerts (2019), at the Image-Based Biomedical Modeling (IBBM) group under Prof. Menze at TUM (2017-2018) and the Computational Imaging (CompImg) group at the Neuroradiology Department at the Rechts der Isar Clinic/TUM in Munich under Dr. Wiestler (2019-2020).
I am also passionate about teaching and have been a TA for Engineering Informatics I, an undergraduate introductory programming course in C, for four years in a row as well as a TA for the courses Heat Transfer and Fluid and Structural Mechanics during my undergraduate studies.
"The BraTS Algorithmic Repository", Invited Talk on October 3, 2019, CBICA Seminar Series; Perelman School of Medicine, University of Pennsylvania
"Fusion of Brain Tumor Segmentations", Invited Talk on January 22, 2019, Neuroradiology Research Colloquium; Klinikum rechts der Isar, Munich
Most of my current projects can be found on Github or in the list below.
Kofler, F., Berger, C., Waldmannstetter, D., Lipkopva, J., Ezhov, I., Tetteh, G., Kirschke, J.S., Zimmer, C., Wiestler, B. and Menze, B., 2020. BraTS Toolkit: Translating BraTS brain tumor segmentation algorithms into clinical and scientific practice. Frontiers in Neuroscience, 14, p.125.
MICCAI 2020 Challenge: Bjoern Menze, Leo Joskowicz, Spyridon Bakas, Andras Jakab, Ender Konukoglu, Anton Becker, & Christoph Berger. (2020, March 20). Quantification of Uncertainties in Biomedical Image Quantification. Zenodo. http://doi.org/10.5281/zenodo.3718912
McKinley, R., Wepfer, R., Grunder, L., Aschwanden, F., Fischer, T., Friedli, C., Muri, R., Rummel, C., Verma, R., Weisstanner, C., Wiestler, B., Berger, C., Eichinger, P., Muhlau, M., Reyes, M., Salmen, A., Chan, A., Wiest, R., and Wagner, F. 2020. Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage: Clinical, 25, p.102104.
Hosny, A., Schwier, M., Berger, C., Örnek, E.P., Turan, M., Tran, P.V., Weninger, L., Isensee, F., Maier-Hein, K.H., McKinley, R. and Lu, M.T., 2019. ModelHub. AI: Dissemination Platform for Deep Learning Models. arXiv preprint arXiv:1911.13218.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M. and Prastawa, M., 2018. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.
I have built and trained models with Keras and Tensorflow, utilized Docker to deploy models for medical image segmentation and use Git to keep everything under version control. I'm used to working in fast paced environments and get things done efficiently. I use my interdisciplinary background in engineering, natural sciences and computer science to see solutions others wouldn't see. All while being able to communicate state-of-the-art research in simple and accessible terms as it is key for efficient collaboration in multidisciplinary teams.