Research

Our research focuses on algorithms and systems for processing and interpretation of sensor data. A special emphasis is on the combination of machine learning with models and prior knowledge. To achieve this, we use combinations of engineering (i.e. probabilistic methods and algorithm design), machine learning, and symbolic AI approaches (e.g. Vector Symbolic Architectures / Hyperdimensional Computing).
Application areas include mobile robotics, automotive, automation, and wherever sensor data processing and interpretation is valuable including industrial applications, human-machine interaction, health care, rehabilitation, and medical applications.

What kinds of problems do we try to solve?

• recognition, classification, and detection

• (sensor) data fusion, analysis, and interpretation

• incorporation of model and prior knowledge in learning-based systems

• generation of descriptive vector representations

• time series analysis

• creation of informative environment models and maps

• decision-making and action execution

For a list of our publications please see here.

Current projects