Kajetan Stanski

I am a PhD student at the Roslin Institute, University of Edinburgh funded by EPIC and supervised by Mark Bronsvoort, Sam Lycett and Thibaud Porphyre. In 2017, I did my integrated MSc in computing science and physics at the University of Glasgow with my thesis on machine learning techniques for classification of Higgs boson events. I worked as a research assistant at the Aalto University in Finland where I was responsible for developing data-driven models predicting physical properties of chemical compounds.

Within EPIC, I contribute to improving veterinary surveillance strategy by applying machine learning methods to animal surveillance data. In Scotland and the rest of GB, surveillance data (e.g. screening test results) are recorded in databases and made available to EPIC researchers. What makes machine learning incredibly useful, is an automatic discovery of patterns and correlations within data. This means that reliable predictions can be made even if a disease spread dynamic is not fully understood or if infection risks are hard to quantify. My aim is to use the machine learning methodology in order to develop better disease control activities and to promote better-informed decision making.

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