I am broadly interested in single-cell genomics and the resulting applications in neurological disease. Single-cell RNA-seq data can be incredibly noisy and drawing meaningful biological conclusions from it is challenging. As a result I will focus on developing computational and statistical methods for the inference of cell state, particularly in respect to biological progress through noisy processes. I am also interested in network inference at the single-cell level and methods to correct for confounding factors in large RNA-seq datasets.
I am supervised by Chris Yau at the Wellcome Trust Centre for Human Genetics / Dept of Statistics and Caleb Webber in DPAG.
I studied Mathematical Physics at the University of Edinburgh followed by a master's degree in Computational Biology at the University of Cambridge. My dissertation was on statistical methods for the inference of spatial cell signalling, carried out at the Systems Biomedicine group at the European Bioinformatics Institute. I have also worked at organisations such as CERN, Institut Laue-Langevin and Agilent Technologies.