About
I am Head of Product at Tanso, a VC-backed startup building carbon accounting software for industrial companies. I lead product strategy and execution across the platform, working at the intersection of complex regulatory requirements and practical software delivery.
My background is in machine learning for medical imaging. I completed my M.Sc. in Biomedical Computing at TU Munich and my Master's thesis at Imperial College London (BioMedIA Group) under Dr. Ben Glocker and Dr. Konstantinos Kamnitsas, focusing on uncertainty estimation in deep learning. I also hold a B.Sc. in Engineering Science from TUM and an honours degree in Technology Management from the Center for Digital Technology and Management.
Before Tanso, I worked at the Computational Imaging and Bioinformatics Lab (CIBL) at Harvard Medical School and Dana-Farber Cancer Institute, at the Image-Based Biomedical Modeling (IBBM) group at TUM, and at Capmo, a Munich startup digitalising the construction industry, where I worked across data science and full-stack software development.
Notable Events
"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
Projects
Most of my current projects can be found on Github or in the list below.
- Out-of-Distribution Detection Framework (Master Thesis) (2021)
- Brain Tumor Segmentation Toolkit, jointly maintained with Florian Kofler (2020)
- Modelhub.ai (2019)
- BraTS Algorithmic Repository (2018)
- Fusion of Brain Tumor Segmentations (Bachelor Thesis)(2018)
Publications
Li, H.B., Navarro, F., Ezhov, I., Bayat, A., Das, D., Kofler, F., ..., Berger, C., et al. 2024. QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. arXiv preprint arXiv:2405.18435.
Berger, C., Paschali, M., Glocker, B., Kamnitsas, K. 2021. Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (pp. 122-132). Springer, Cham.
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.
Other Interests
- TEDxTUM, Team Lead Operations (2019), Member (2018, 2020) - we're a team of volunteers organizing TEDx-style events at the TU Munich with up to 600 guests to give local and novel ideas a stage
- Volunteering for different organizations, for example the Karate World Championship in Austria (2016) or at the Bits & Pretzels Startup Festival in Munich (2018)
Toolkit
My background spans machine learning research, full-stack software development, and product management. Fluent in Python; experienced with Keras, PyTorch, and Docker for ML model development and deployment. Comfortable working across the full product lifecycle — from user research and discovery to sprint planning and cross-functional delivery.
I use my interdisciplinary background in engineering, natural sciences, and computer science to find solutions others wouldn't see, and to communicate complex technical work in accessible terms — key in the multidisciplinary teams I work with.