Since Sep. 2015, I am a PhD student in the Brain-State Decoding Lab at the University of Freiburg which is headed by Dr. Michael Tangermann. My primary research focus is on Brain-Computer Interfaces (BCis). These are systems which can translate neuronal signals (e.g. brain signals measured from the EEG) into control commands (e.g. to allow communication or enable wheelchair control for patients, or to enrich games).

In BCI, my research is on two aspects. On the theoretical side, I work on unsupervised machine learning methods that enable learn to calibrate itself only by using unlabeled data (data where the user's intention are unknown). On the practical side, I am involved in a project exploring the feasibility and effectiveness of a training based on Brain-Computer Interfaces for patients with language deficits (aphasia) after a stroke.

Before being a PhD student, I did a BSc. of Mathematics in Potsdam (Germany) from 2009-2012. After studying abroad for one semester in Perth (Australia) I did a MSc. of Applied Mathematics / Scientific Programming as part of a European program called COSSE (Computer Simulation for Science and Engineering) from 2013-2015. In this program, I spent the first year at TU Delft (Netherlands) and my second year at KTH Stockholm (Sweden) where I wrote my Master Thesis in computational neuroscience.

I used the gap year between my Bachelors and Masters to travel around Australia, New Zealand and South-East Asia. During that period, I first really got in contact with photography. Some of my pictures are shown on this homepage.


(Apr 2018): We have been nominated for the BCI Award again: This time with our work on BCI-supported language training. Check out our video below. The winner will be crowned at the 7th International BCI Meeting May 21 – 25, 2018 at the Asilomar Conference Center in Pacific Grove, California, USA. There, we will also have a workshop on unsupervised learning. Update: We won the 2nd price in the BCI award! Yay! News

(Apr 2018): Our new paper on unsupervised learning appeared in the IEEE computational intelligence magazine. In this paper, we review different unsupervised learning approaches for ERP-based BCIs and compare three of them in an online study. Link

(Jan 2018): We presented a workshop about unsupervised learning for BCIs at the applied machine learning days in Lausanne Link

(Sep 2017): I won the best talk award for an oral presentation at the 7th Graz Brain-Computer Interface Conference 2017. I presented our joint work on unsupervised learning by mixing model estimators. News

(Aug 2017): Our work on unsupervised learning has been nominated for the BCI Award 2017, an international award with more than 50 competing groups. Poster

(Jul 2017): I won the best talk award at the first Neuroadaptive Technology (NAT 17) conference in Berlin. I presented work about unsupervised learning with learning from label proportions (LLP).