Marc Rußwurm

I aim to develop state-of-the-art machine learning methods for timely and relevant applications, such as the classification of vegetation from satellite time series or the detection of marine debris in the oceans. My Ph.D. research focused on deep learning models for satellite time series classification and crop type mapping. Today, I deploy general deep learning models on a global scale which requires tackling distribution shifts with transfer learning on a variety of different applications. In particular, I train deep learning models with the model-agnostic meta-learning algorithm (MAML) to tackle a variety of different problems with few labeled samples.

I did my Ph.D. research at the Technical University of Munich at the Chair of Remote Sensing Technology. During my Ph.D., I had the opportunity to visited the European Space Agency and the University of Oxford as a participant in the Frontier Development Lab in 2018. Also, I could visit the IRISA Obelix Group in Vannes, France, and the Lobell Lab at Stanford. Today, I am a postdoctoral researcher at the EPFL Laboratory for Environmental Computer Science and Earth Observation (ECEO) where I develop state-of-the-art machine learning methods for meaningful and important applications.