Hannah Spitzer – Machine Learning and Data Science

Our research group uses computational analysis and machine learning algorithms to identify patterns and relationships from complex, multimodal, and multiscale datasets of the brain in health and disease. We collaborate with national and international partners to apply novel methods from the fields of computational biology and computer vision to obtain new insights from brain datasets, with a primary focus on neurovascular and neurodegenerative phenotypes. We focus on learning interpretable representations from complex high-dimensional datasets that allow us to readily explain the biological relevance of results and ultimately understand underlying disease mechanisms.

We are particularly interested in the following areas:

1) Spatial Omics:

We aim to gain a deeper understanding of the cellular organisation of the brain by analysing gene and protein expression in a spatial context. Using spatial omics datasets, including 10x and MERFISH, we extract spatial gene and protein expression patterns and relate them to morphology and single-cell RNA sequencing data. For this, we have recently developed the spatial omics analysis toolbox squidpy (Palla, Spitzer, et al, Nature Methods 2021) and the subcellular analysis method campa (Spitzer, Berry et al, Nature Methods 2023, in press) in collaboration with Fabian Theis at Helmholtz Munich.

2) Image Representation Learning:

We establish pipelines for extracting interpretable features from histology and neuroimaging data using advanced deep learning algorithms. As part of the MELD project in collaboration with Sophie Adler and Konrad Wagstyl at University College London, we are currently using graph neural networks to identify focal cortical dysplasia lesions in a large cohort of epilepsy patients (Spitzer, Ripart et al, Brain 2022). Our goal is to develop and apply advanced image analysis techniques that will help us to better understand structural brain changes associated with neurodegenerative diseases.

3) Cross Modal and Cross Dataset Learning:

We aim to combine datasets across diseases and modalities into a multimodal and multi-scale brain atlas. By integrating data from different sources and diseases, we hope to identify features that are conserved across diseases and study correlations between modalities. This will enable us to better understand disease phenotypes and mechanisms which might lead to more effective treatment strategies.

Our lab is a part of the Institute of Stroke and Dementia Research, and we are affiliated with the Computational Health Center at Helmholtz Munich. We are currently looking for talented and motivated Masters students, PhD students and postdocs to join our team! 

Please contact hannah.spitzer@med.uni-muenchen.de to learn more.