The partners of MUDS are committed to jointly move towards data-enhanced science, combining methods development in data science with the application domains: life sciences, medicine and health, earth observation, and robotics.
Life Sciences
Biomedicine
With the availability of exponentially increasing datasets across molecular and cell biology, the field is in strong need for better data mining and machine learning-based analytics. Applications include image classification or visualization and analysis of genomics data sets.
Medicine & Health
Medicine & Health
Questions applied to clinical and epidemiological challenges are addressed: As larger patient and clinical sample datasets become available, we combine cutting-edge data engineering and machine-learning with model-based approaches for deeper insights and improved solutions.
Earth observation
Earth observation
Big data analytics and knowledge discovery methods are urgently required to turn data from applications such as land cover/land use classification, 3D reconstruction, atmospheric trace gas retrieval, fusion of satellite and geo-relevant internet data, e.g. from social networks, into scientific knowledge and to value for society.
Robotics
Robotics
The combination of robotics and AI is having an increasing societal and economic impact. Data-driven approaches are essential to complement analytical models to enable robotic systems to deal with uncertainty, discover new skills autonomously, to monitor their health and optimize their behavior over time.
Data science is the methodology linking these application areas. While the detailed scientific approaches are different across the four application domains, they share the feature that they all lie between the extremes of purely data-driven versus model-driven approaches. Within MUDS, we aim at exploring novel ways to connect these two poles, thus enabling novel approaches and results.
