Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data
Spatialproteomics is a scalable Python toolbox that enables the end-to-end analysis of highly multiplexed immunofluorescence imaging data.
Professional Summary
I am a PhD candidate at the European Molecular Biology Laboratory (EMBL) in the group of Wolfgang Huber. My current work focuses on developing open-source software for the processing and analysis of large-scale spatial omics datasets (spatialproteomics). I am passionate about applying computational methods, including machine learning and AI, to answer questions of biomedical relevance. Previously, I studied bioinformatics at the Technical University of Munich (TUM) and Ludwig Maximilian University of Munich (LMU). I also contribute to open-source projects such as scverse.
Education
PhD in Bioinformatics
2022
EMBL Heidelberg
MSc in Bioinformatics
2020
2022
TUM & LMU Munich
BSc in Bioinformatics
2017
2020
TUM & LMU Munich
Interests
Bioinformatics
Computational Biology
Spatial Omics
Image Analysis
Artificial Intelligence
Machine Learning
Multi-Omics
Precision Medicine
Statistics
Featured Publications
A pathway coordinated by DELE1 relays mitochondrial stress to the cytosol
We identified a previously unknown signaling pathway that relays mitochondrial stress to the cytosol and activates the integrated stress response.
evelyn-fessler
Recent Publications
Teaching
Representation Learning Workshop
Using DINOv2 representations to predict subcellular localization of proteins.
Spatialproteomics Workshop
Using spatialproteomics to analyze highly multiplexed fluorescence images.
Data Analysis Workshop
Introduction to data analysis and visualization in Python.