Hannah Spitzer

Hannah Spitzer is a Research Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig Maximilian University of Munich and an associated Research Group Leader at Helmholtz Munich's Computational Health Center. She leads the Spitzer Lab, focusing on computational analysis of multimodal brain datasets to advance understanding of neurovascular and neurodegenerative diseases.

Her educational background includes:

• PhD in Computer Science from Heinrich-Heine University Düsseldorf and Research Center Jülich (2015-2020)
• Master's in Computer Science from RWTH Aachen (2013-2015)
• Bachelor's in Computer Science from RWTH Aachen (2009-2013)

Dr. Spitzer's research integrates computational biology and machine learning to decode brain complexity, with emphasis on spatial omics analysis, interpretable image representation learning, and cross-modal data integration. Her group develops tools like squidpy and campa for spatial omics while applying graph neural networks to epilepsy lesion detection through the international MELD project, prioritizing biological interpretability in AI models.

Recent publications reveal strong trends in leveraging graph neural networks for subtle brain lesion detection and creating computational frameworks for spatial omics integration. Her work consistently bridges advanced machine learning with clinical neuroscience to uncover disease mechanisms in neurodegeneration and vascular disorders.

Dr. Spitzer actively mentors students including current PhD candidate Beatrice Guastella and alumni Deniz Fettahoglu (MSc) and Katia Berr (PhD). Her lab operates through major collaborations including the MELD epilepsy consortium and Helmholtz Imaging Project, with funding supporting computational pipeline development for small-vessel disease prediction and multimodal brain atlasing.

The Spitzer Lab comprises postdoc Wasim Aftab and PhD student Beatrice Guastella, working on computational pipelines that integrate histology, spatial omics, and neuroimaging data to decode brain disease mechanisms through interpretable AI approaches.