Research Centre of Cognitive Computing in Life Sciences
With Cognitive Computing, we offer new solutions for the life sciences that are based on the fundamental understanding of man and machine as a learning system.
The Centre for Cognitive Computing for Life Sciences deals with the development and use of computational methods and models for the field of life sciences, whose properties are inspired by the learning and adaptive abilities as well as self-organisation principles of natural systems. The solutions developed support demanding human activities and decision-making processes or can be used for process automation. The following general aspects are central to our research and development: learning ability/adaptability of the systems, context-bound solutions (application context in the life sciences), systemic consideration of the application and the context.
The centre is divided into different research groups, each of which focuses on certain methods or application domains.
Bio-Inspired Methods and Neuromorphic Computing
The research group is methodologically specialised in the development and use of adaptive algorithms and nature-inspired methods (evolutionary algorithms, Physics methods). Furthermore, the specialisation also includes the use of neuromorphic technologies and the development of hybrid approaches that combine models of complex systems with data-driven solutions. The areas of application include all areas of the life sciences with a focus on the health domain as well as laboratories and production.
Autonomous Systems and Reinforcement Learning
The research group is methodologically specialized in reinforcement learning, unsupervised and semi-supervised learning as well as human-in-the-loop machine learning. The methodology therefore also covers topics such as online learning and agent-based systems.
Applications span all areas of the life sciences. A specific focus is on the development of machine learning solutions for applications in biotechnology.
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. We 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.
Digital Environment and Sustainability
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. We have a data science, machine learning and modeling approach to our problems. A special methodological focus is on modeling with discrete systems such as cellular automata and on deep learning methods.
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
Head of Research/Focus Area, Digital Environment
Programme Director, MSc specialisation in Applied ...
Head, Autonomous Systems and Reinforcement ...
Head, Predictive Analytics Group (FS)
Stability of self-organizing net fragments as inductive bias for next-generation deep learning
We recently released "A Theory of Natural Intelligence", proposing a possible key to the emergence of intelligence in biological learners. Goal of this fellowship is to develop a technical implementation of the concept of self-organizing netfragments within contemporary deep artificial neural nets. ...
Investor and Stakeholder Tools for Tracking Companies’ Climate Commitments, Greenwashing and ESG Trends
This project aims to create a set of science-based methods to systematically detect potential greenwashing in corporate communication, as well as signals for green innovation and technologies. By applying the methods to thousands of stocks, we intend to design novel, ESG-proof financial products. ...
An experimental framework to allow evidence-based sustainability policymaking
This research project evaluates the feasibility of using a mathematical decision framework (based on the 2019 Nobel laureates in Economy who developed an experimental approach for improving policy in poverty through field experiments) in sustainability policy, and achieving a software-supported and data-driven ...
Reinforcement Learning Analysis Framework
The aim of this project is to implement a framework that facilitates the development of RL solutions for real-world applications. This is necessary since the academic literature usually focuses on specific algorithms and approaches differ widely for different regions in the highly complex RL problem space. ...
Horn, Claus; Nyfeler, Matthias; Müller, Georg; Schüpbach, Christof,
Drone radio signal detection with multi-timescale deep neural networks [paper].
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,
Increase of skin temperature prior to parturition in mares.
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,
Circadian rhythm tracking using core body temperature estimates from wearable sensor data.
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,
Explainable deep learning for medical time series data [paper].
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
Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo,
How (not) to measure bias in face recognition networks [paper].
Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
Artificial Neural Networks in Pattern Recognition.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Lecture Notes in Computer Science ; 12294.
Available from: https://doi.org/10.1007/978-3-030-58309-5_10