R. Cavill

Rachel is an assistant professor in the department of Data Science and Knowledge Engineering.  Her research has several key themes; 

• Method development for data integration of different types of omics data.  For instance, metabolomic and transcriptomic data.  
• Domain adaptation in bioinformatics.  The application of advanced machine learning methods that allow learning to take place across different domains (eg. different organisms, different tissues or different batches of data).
• SetPCA https://dke.maastrichtuniversity.nl/rachel.cavill/  A method that uses background knowledge about the variables (and the sets they form) to make multi-variate methods (eg PCA/PLS) more interpretable.
• Biological images - applying deep learning or feature extraction pipelines to biological images.