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.
Professional career history
- PhD University of York, England, Title: Multi-Chromosomal Genetic Programming
- Research Assistant, Computer Science Department, Robert Gordon University, Aberdeen
- Research Associate, Computational and Systems Medicine, Imperial College London
- Research Fellow, Toxicogenomics, Maastricht University