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.
About us
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.
Methods we use
Combination of model-based approaches and data-driven machine learning:
Self-organizing and complex systems
- Dynamical systems, cellular automata, physics-constrained DL
Neuromorphic computing
- 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
Examples of application domains
- 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
Autonomous Learning Systems (ALS)
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.
Advanced Signal Analytics (ASA)
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.
Computational Environment Group (CEG)
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.
Neuromorphic Computing Group (NCG)
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.
Predictive Analytics
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.
Teaching Activites
The centre includes teaching engagements at BSc and MSc level as well as in continuing education. At BSc level, basic subjects in mathematical and physical modelling, statistics and information processing are offered in all study programmes of the department. Furthermore, teaching includes specific subjects in the field of data science with a focus on "machine learning, neural networks, signal and image analysis" in the institute's own BSc ADLS and MSc ACLS programmes and in continuing education. Thirdly, specific subjects are offered, especially in the "Digital Environment" specialisation of the ADLS.
Team Cognitive Computing in Life Sciences
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Head of Research/Focus Area, Digital Environment
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Programme Director, MSc specialisation in Applied ...
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Head, Autonomous Learning Systems research group
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Head, Predictive Analytics Group (FS)
Projects
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AWACS – Animal Welfare Assessment and Control System for Fish Farms
Urban Blue offers software solutions for land-based aquaculture farms, which include technical and operational aspects. With this innovation project, fish-based aspects are developed to complete the software. This way Urban Blue can provide a comprehensive farm management system that incorporated fish health and ...
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Optimizing plant health in indoor farming using reinforcement learning
The research question we tackle in this project is "How can we automatically optimize the growth path of indoor-farming plants?". To achieve this goal, we develop new algorithms to measure plant health and growth. We then evaluate reinforcement learning based approaches to optimize these metrics. ...
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Transformer Networks for Drone Signals
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Shapescience – AI for morpholigcally based fruit variety recognition
Pomological and molecular genetic methods have been the tools of choice to describe fruit varieties and their characteristics. Thanks to the possibility of using 3D scanning technology and machine learning, there is a revival of phenotyping, i.e. the typification of fruits based on their appearance. The Shapescience ...
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Optimization of online education systems with reinforcement learning
The research question we tackle in this project is "Can we simultaneously increase student engagement and retainment by optimizing the order of exercises in online education courses?". To achieve this goal, we develop new algorithms to measure student engagement and retainment. We then evaluate reinforcement ...
Publications
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Epper, Pascale; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Kaeser, Rebekka; Burger, Dominik,
2023.
Increase of body temperature immediately after ovulation in mares.
Journal of Equine Veterinary Science.
127(104565).
Available from: https://doi.org/10.1016/j.jevs.2023.104565
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Horn, Claus; Nyfeler, Matthias; Müller, Georg; Schüpbach, Christof,
2022.
Drone radio signal detection with multi-timescale deep neural networks [paper].
In:
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.
IFSA Publishing.
pp. 140-143.
Available from: https://doi.org/10.21256/zhaw-27185
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Müller, Antonia; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Burger, Dominik,
2022.
Increase of skin temperature prior to parturition in mares.
Theriogenology.
190, pp. 46-51.
Available from: https://doi.org/10.1016/j.theriogenology.2022.07.007
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Rerabek, Martin; Schiboni, Giovanni; Durrer, Lukas; Oliveras, Ruben; Eib, Philippe; Rouchat, Fabien; Probst, Anja; Schmidt, Markus; Kryszczuk, Krzysztof,
2022.
Circadian rhythm tracking using core body temperature estimates from wearable sensor data.
In:
7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022.
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Frick, Thomas; Glüge, Stefan; Rahimi, Abbas; Benini, Luca; Brunschwiler, Thomas,
2021.
Explainable deep learning for medical time series data [paper].
In:
Wireless Mobile Communication and Healthcare.
International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020.
Cham:
Springer.
pp. 244-256.
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