Markus Ferdinand Dablander

Background

About Markus Ferdinand Dablander

I am a mathematician who is currently pursuing a doctoral degree at the Mathematical Institute of the University of Oxford. I am also a member of the Center for Doctoral Training in Industrially Focused Mathematical Modelling (InFoMM CDT) and of the Oxford Protein Informatics Group (OPIG). During my undergraduate degree, I focussed on pure mathematics and its rigorous methodology. Since then, I have additionally become interested in the applied and data-driven side of the mathematical sciences. I have developed a particular focus on mathematical data science, programming, graphs and networks, deep learning, artificial intelligence and advanced statistical machine learning. During my master studies at University College London and the University of Vienna, I conducted a variety of data science research projects. These projects included the detection of neonatal brain injury, causal inference in metabolomic networks, the analysis of cerebral fMRI-data, the prediction of chaotic dynamics in time series data, and the computationally efficient solution of high-dimensional parametric partial differential equations via deep learning. In my current DPhil (= PhD) project at the University of Oxford I am collaborating with the research company Lhasa Limited to investigate novel graph-based machine learning techniques and their applications in chemistry and computational drug discovery.