Cognitive Computing in Life Sciences (ZHAW ICLS) Verified listing

The Research Centre for Cognitive Computing in Life Sciences develops and applies computational methods and models inspired by learning and self-organisation principles of biological neural systems to problems in life sciences, such as activity recognition, bio-signal processing, environment monitoring and assistive autonomous systems.

With cognitive computing, we develop new software technology, algorithms, and systems for signal processing, data analytics and process control in life sciences.

Advanced Signal Analytics - Dr. Matthias Nyfeler 

This research group works on the analysis, modelling and classification of signals as as well as statistical modelling and consulting. Our experts use physical and statistical models as well as deep learning applied to problems in the life sciences such as agricultural systems, chromatography or signals from drones. The group consists of data science and statistics experts with backgrounds in theoretical and applied physics and mathematics with a long experience of research and teaching in the life sciences.

Computational Environment - Dr. Martin Schüle 

The research group focuses on the modeling of natural systems and their interaction with humans. This also includes sustainability topics in a more general context such as in view of social and economical questions. Our experts approach the challenges with data science, deep learning and modeling. A special methodological focus is on deep learning methods, multimodal learning with natural language processing and on modeling with discrete systems such as cellular automata.

Neuromorphic Computing - Prof. Dr. Yulia Sandamirskaya

The research group develops advanced neural-network based algorithms, software libraries, and systems with the new generation of computing chips – brain-inspired neuromorphic sensing and computing hardware. Our experts focus on perception, motion planning, and control for robotic actuators with applications in there fields: healthcare, agriculture, food processing, and smart environments. Our research group follows a human-centered design approach to develop new generation of physical AI systems that are power-efficient, adaptive, and safe.

Predictive Analytics - Dr. Krzysztof Kryszczuk

The research group's focus is on applied research in statistical modeling and machine learning for pattern discovery, as well as data mining, pattern recognition, and forecasting in life sciences. Our experts have a proven track record in the areas of med-tech, personalized health and sports analytics. The group's expertise lies in the fusion of heterogenous information sources and ensemble methods, in particular for time series and image/video analytics. In the context of Industry 4.0, the group conducts research in the area of predictive and prescriptive maintenance.