Summary
Hannah Spitzer is a Principal Investigator in biomedical machine learning based in Munich with a decade of experience developing multimodal ML methods that fuse histology, spatial and single-cell omics, and neuroimaging to advance brain research and personalized treatments. She leads a data analysis group at LMU Klinikum and Helmholtz Munich, translating cutting-edge supervised, self-supervised and unsupervised representation learning into tools clinicians and wet-lab researchers can use. Her postdoctoral work produced interpretable deep learning models for lesion detection, disease progression forecasting, and subcellular multiplexed image analysis, and contributed to multiple open-source Python packages now used in clinical research. Trained as a computer scientist with a PhD-level research background in high-resolution histological segmentation and 3D reconstruction, she combines systems-level engineering—HPC pipelines and large-image workflows—with biological insight to reveal cytoarchitectural variability. Notably, she has a track record of building methods that move from algorithm to hospital deployment through close interdisciplinary collaboration.
11 years of coding experience
4 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at RWTH Aachen University