Research Centre of Cognitive Computing in Life Sciences
With cognitive computing, we develop new software technology, algorithms, and systems for signal processing, data analytics and process control in life sciences.
The Research Center of 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.
Combination of model-based approaches and data-driven machine learning:
Self-organizing and complex systems
- Dynamical systems, cellular automata, physics-constrained DL
- Event-based vision, neural motion planning and control, autonomous learning, power-efficient AI, real-time AI
Classical and deep learning based machine learning
- NLP, Generative AI, Multi-modal modelling, explainable AI
- Reinforcement learning, Bayesian modelling, predictive analytics, signal processing
- Environmental systems and sustainability
- Remote sensing
- Spectral analysis in food industry and material science
- Protein engineering
- Process automation
- Predictive maintenance
- Bio-signal analysis and sports analytics
- Smart farming
- Collaborative and assistive robotics
Group leader: Dr. Claus Horn
The research group is specialized in developing next-generation AI systems by integrating deep learning and reinforcement learning techniques to create autonomous AI agents. These agents can be applied to a wide range of process automation tasks across the life sciences. A special focus is the development of AI solutions for protein engineering.
Group leader: 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.
Group leader: 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.
Group leader: 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.
Group leader: 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.
Employing Natural Language Processing to identify inconsistencies in companies’ non-financial communication
NLP &ML tools for Swiss asset managers and owners for identifying inconsistencies in companies’ financial and non-financial communication
PE(K)O Sustain – Physically modified oils as sustainable alternative to tropical fats for the baking and sweet goods industry
Classification of drone signals
Predictive Analytics for Hospital Supply Chain Management
Preliminary/feasibility study for a comprehensive AI-based solution in hospitals for demand-oriented assortment and efficient inventory Management ordering with a focus on security of supply and cost Efficiency planning of reprocessing planning of personnel deployment cost and income planning ...
Detection of drone signals
Epper, Pascale; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Kaeser, Rebekka; Burger, Dominik,
Journal of Equine Veterinary Science.
Available from: https://doi.org/10.1016/j.jevs.2023.104565
Horn, Claus; Nyfeler, Matthias; Müller, Georg; Schüpbach, Christof,
Yurish, Sergey Y., ed.,
Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence.
4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI), Corfu, Greece, 19-21 October 2022.
Available from: https://doi.org/10.21256/zhaw-27185
Müller, Antonia; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Burger, Dominik,
190, pp. 46-51.
Available from: https://doi.org/10.1016/j.theriogenology.2022.07.007
Rerabek, Martin; Schiboni, Giovanni; Durrer, Lukas; Oliveras, Ruben; Eib, Philippe; Rouchat, Fabien; Probst, Anja; Schmidt, Markus; Kryszczuk, Krzysztof,
7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022.
Frick, Thomas; Glüge, Stefan; Rahimi, Abbas; Benini, Luca; Brunschwiler, Thomas,
Wireless Mobile Communication and Healthcare.
International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020.
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; 362.
Available from: https://doi.org/10.1007/978-3-030-70569-5_15