My background is in genetics, both at the bench and in silico. During my PhD at the University of Cambridge/Wellcome Trust Sanger Institute, I investigated methods for large-scale genetic studies of malaria parasites. As a post-doc, I developed a robotic system to automate the parasite culture required for such studies, allowing protein tagging studies to be conducted at scale. I remain interested in how we can use automation to increase the scale of biological discovery. The reams of data that recent biological approaches generate will require new approaches to data analysis, and I think that deep-learning approaches will play an essential role in turning this data into actionable insights. I am excited to be spending a year at Google AI, immersing myself in these methodologies and learning from experts in the field. I aim to further pursue the biological applications of AI during the residency, and am currently focusing on creating networks that interpret the sequences of amino-acids that make up proteins, with a view to developing techniques to classify proteins of unknown function and potentially eventually facilitate protein design through the use of meaningful embedding spaces. My other interests include building generative models of microscopy data, and developing smarter robot scientists.