I aim to develop state-of-the-art machine learning methods for meaningful and important applications, such the classification of vegetation from satellite time series or the detection of marine debris on 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 shift 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 labelled samples.
A full list of publications available via google scholar