Area of expertise
Bayesian inference, MCMC, Machine learning, Deep learning
Area of interest
1. Application of deep learning development of new methods (and more general machine learning) to biomedical cases like cell segmentation, histological image classification, MRI segmentation for multiple sclerosis, etc.
2.Application of MCMC techniques for Bayesian inference in survival models (hierarchical, spatial dependence, etc.)
COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.
Validation of diagnostics
Estimation of Disease prevalence
Confidence in Disease absence – proof of disease freedom models
Early warning systems for epidemics
Grant Holder: Faculty of Public and One Health, University of Thessaly,
Greece
Start of Action: 25 October, 2019
End of Action: 24 April, 2024
Entry into force: 26 June, 2019
Action email: info@harmony-net.eu
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