**Guest PhD student****Name: Julius Lohman****Email: juliuslohmann@uni-muenster.de****Phone: +31 (0)53 489 1116**

General informations

2014 - 2017, FAU Erlangen-Nürnberg: MSc Mathematics (minor Computer Science)

2018 - 2019, FAU Erlangen-Nürnberg: Research assistant

2019 -, WWU Münster: PhD student

02/2020 - 03/2020: 1-month secondment at Clinical Science Systems within the NoMADS project

**Research interests: **

My PhD topic is in the field of optimal transport theory. During the secondment I will work on a problem concerning the automated detection of interictal epileptiform discharges in EEG recordings. One can think of optimal transport theory relating to machine learning in signal analysis as follows. Finite weighted graphs are an important structure (e.g. for nonlocal approaches) in deep learning. They are used in branched transport as approximation of a continuous mass flux. The branched transport problem is a non-smooth and non-convex problem on Radon measures and thus there are parallels to the theory that is used when studying the continuum limits of machine learning problems in their variational form. Transportation theory is also used to study the limits of discrete operators, such as the Laplace operator on graphs. This theory also plays a role in the transition to continuous nonlocal methods.