Research Scenarios & Methods

In Robotics, a considerable challenge is to transfer human manipulation and locomotion skills using learning from demonstration to robotics. Recording with human interaction is time consuming and expensive. Ideally, the robot should learn directly from observing complex demonstrations.

In Quantitative Biology & Smart Microscopy, a main challenge is to extract specific quantitative information from imbalanced, rare imaging data sets with the goal to improve effectiveness and reproducibility in clinically relevant fields.

In Predictive Medicine, it is a challenge to support doctors with trustworthy decision support systems for comprehensive sets of diseases based on few, multimodal data.

We will research novel brain-inspired methods and architectures, with the aim of improving capabilities in few-shot and continual learning.

We will study data-centric workflows for annotating vision models that support human feedback, incorporate multimodal data, and learn complex motion by demonstration.

Holistic Evaluation, Fairness, Robustness. We will examine how to automate and holistically evaluate FMs, their robustness, fairness, and explainability.

We will refine text presentations in a multimodal setup, based on existing FMs.