Damian Borth (Artificial Intelligence and Machine Learning)

One of our fundamental research direction is representation learning with deep neural networks. Being able to understand, to disentangle, and to control the latent factors of the underlying data would make deep learning more scalable and more trustworthy. It would allow us to gradually move from supervised to unsupervised learning and therefore render model training less dependent on large amounts of labels. Additionally, having control over the latent space of the data provides interpretability of model inference, increase robustness, and allows to characterise model parameter in the weight space.

One of our research goals is to make the large amount of visual and audio content accessible. This includes besides the analysis of such data also the generation of synthetic data with natural characteristics. Currently we focus our research on neural Text-to-Speech (TTS) generation for the German language. In this scope we aim to develop novel approaches for speaker embedding, prosody control, and voice transfer.

We are interested in how we can utilize the vast amount of remote sensing data for earth observation. In particular, we aim to develop novel deep neural networks architectures able to consume the full ensemble of spectral bands from today’s satellites for applications such as climate change monitoring, natural disaster recovery, or changes in land use and land cover over time.

Our research in this area covers the analysis and forecasting of financial time-series data on the one hand and anomaly detection in transaction data on the other hand. Our research focuses in particular on financial markets and the audit domain.