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Ann-Kathrin Schalkamp

Dr Ann-Kathrin Schalkamp

Research student

School of Medicine


My research interest lies in the intersection of machine learning, medicine, and neuroscience. My goal is to contribute to the advancement of personalised medicine through research in computational neuroscience/medicine. 

I focus on neurodegenerative disorders, especially Parkinson's Disease, and try to understand the heterogeneity observed within them. Deeply phenotyped cohorts allow me to investigate a whole person on multiple scale levels (genetics, omics, imaging, clinical data, environment). I make use of Machine Learning, Bayesian Modelling, and occasionally Deep Learning  to handle complex, high dimensional data.







I am interested in developing methods and applying Machine Learning for the advancement of healthcare towards personalised medicine.

Currently, diagnosis and treatment selection, especially in psychiatry, rely on self-report and subjective decisions made by physicians. Augmenting their decision process by means of machine learning could largely fasten and objectify this process. Visualizing medical data in a human comprehensible way or providing diagnoses and related uncertainties are possible augmentations. An automatic, data-driven assessment of a patient’s health status can considerably alleviate the workload of physicians, enable early disease detection, and extract meaningful insights from the abundance of the medical data available.

I have a background in Cognitive Science with a focus on Machine Learning. Over the course of my education I had the opportunity to work with a wide range of data modalities: brain imaging, electroencephalography, genetics, biospecimen, and clinical data. During my PhD I get to work with datasets that provide all these data modalities and learn about a new resource, omics. I work with UK Biobank, PPMI, OPDC, and ADNI. I do most of my analyses in python but also have experience with bash, R, Matlab, and toolboxes like plink and SPM.

In general, I am an advocator for open science and reproducibility and try follow these principles in my own research.


Stratifying deeply phenotyped Parkinson’s patients with brain imaging and blood-based immune signatures