Research Centre of Computational Health
The devision of Computational Health is specialised in the confluence of data-driven and mechanistic modelling approaches in medicine and biology. The methods encompass machine learning for image and signal analysis, graphical networks, optimization of stochastic models and physiological simulations.
The centre Computational Health addresses fundamental questions in biology and medicine using computer-assisted, data-driven methods. Important tools are machine learning for image and signal analysis, parameter estimation for differential equation systems and multiphysics simulation. Effective validation strategies are used to deal with the unknown.
The research group specializes in modeling biological and medical systems. New approaches are developed to simulate physiological processes and to predict pathological changes. In particular, in-depth knowledge of biological/physiological processes is incorporated into multi-physics simulations.
The group develops algorithms for parameter and uncertainty estimation of physically motivated stochastic models. In particular, machine learning methods are combined with Bayesian modeling for dimensionality reduction. These methods are widely used in medicine and life sciences.
The research group applies machine learning techniques to interpret medical image data. This way, features are extracted for the characterization of disease patterns and for use as diagnostic markers. Of particular interest are the radiomic and morphological analysis of diagnostic medical imaging data. The group pursues the goal of establishing reproducible, image-based biomarkers by means of explainable artificial intelligence and ensuring their clinical utility.
The research group uses statistical and machine learning methods to model and uncover causal relations in medical data, especially to study pathophysiological processes. Categorial patient data and imaging date, e.g. magnetic resonance imaging, are processed to extract clinical knowledge.
The research group studies data from wearables and biosensors using time series analysis and combines them with biological-physical models to robustly characterize physiological systems. These data sources are used for Patient Reported Outcomes in clinical practice and for the further development of patient-centered medicine.
BISTOM - Bayesian Inference with Stochastic Models
In essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for making probabilistic predictions. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability ...
A cloud-based IoT approach for food safety and quality prediction
Safety and quality prediction are topical issues in food industry. We are developing a novel IoT approach in the framework of a collaboration with Genossenschaft Migros Zürich (GMZ), ZHAW (represented by the institutes IAS and ILGI) and Axino Solutions AG. The main goal of the project is to provide a robust, ...
PhD Network in Data Science
Swissuniversities project to promote PhD programs at ZHAW in the field of Data Science.
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