I am Professor for Bioinformatics at the University of Applied Sciences Weihenstephan-Triesdorf and I am leading the bioinformatics division at the TUM Campus Straubing for Biotechnology and Sustainability.
I studied Bioinformatics at the University of Applied Sciences Weihenstephan-Triesdorf and conducted my Diploma thesis at the Max Planck Institute for Biological Cybernetics and the Max Planck Institute for Developmental Biology in Tübingen (Germany). During my studies I did two research internships. The first one I did in 2008 at the University of Cambridge (UK) and the European Bioinformatics Institute (EMBL-EBI). The second one I did at the University of New South Wales in Sydney (Australia).
After I completed my studies in bioinformatics at the University of Applied Sciences Weihenstephan-Triesdorf, I gained deep insights in analysing complex and large-scale biomedical data using state-of-the art machine learning and data mining techniques as a Ph.D. scholar at the Max Planck Institute for Intelligent Systems and the Max Planck Institute for Developmental Biology in Tübingen. I developed novel variant calling and alignment algorithms to accurately predict deletions and insertions using a Support Vector Machine from Illumina paired-end next generation sequencing data. Further, I focused on the analysis of genetic and phenotypic data of large cohorts of individuals to gain a better understanding of the genetic architecture of complex traits – also known as genome-wide association studies (GWAS). For this purpose, I developed novel data analysis pipelines and applied state-of-the-art machine learning algorithms and statistical methods to efficiently process and analyse this type of data, while at the same time allowing the integration of prior biological knowledge (e.g. protein interaction networks). To simplify the process of conducting GWAS I developed easyGWAS (https://easygwas.ethz.ch) a web- and cloud-service for performing, analysing, sharing and visualising GWAS in a web-browser. Further, I investigated methods for genome variant annotation that quantify the disease risk of a patient based on his or her sequence variants.
After my PhD, I moved as postdoctoral researcher to the ETH Zürich. There, I developed novel algorithms in the field of significant pattern mining to reduce the number of testable hypothesis for association studies. Further, I developed a platform to collect publicly available phenotypes for the model species Arabidopsis thaliana, called AraPheno.
In general I am highly interested in bioinformatics, machine learning and its applications. It is fascinating to see how to gain a better understanding and how to draw conclusions from data that is analysed using state-of-the-art machine learning and data mining techniques.