Patrick Gibbs

PhD thesis: TBC

Research interests:

1. Quantitative Genetics
2. Machine Learning
3. AI in medicine

My PhD research project will focus on using population-wide electronic health records to develop prediction models for disease and health trajectories. I also hope to extend such models with genetic data. One outcome of this work would be the ability to use DNA and medical history to predict clinically relevant features such as the probability of developing disease, or response to medications. I am also interested in how machine learning models that predict health trajectories can be used to understand how changes in demography at a population level (e.g. ageing population) might exhibit changes in disease prevalence. Thus, I hope to develop tools that can predict disease at an individual level, and allow for health system planning by predicting disease at a demographic level across the UK population.

Who or what inspired you to pursue your research interests?

In my undergraduate studies I majored in genetics with a profound interest in how pieces of DNA, each with its own unique history, influence much about who we are—our appearance, personality, susceptibility to diseases, etc. Throughout my studies I also took classes in Math and Computer Science, and I developed an interest in how a quantitative lens provides a unique and powerful way to understand concepts in natural science. In my PhD I hope to continue this research direction of leveraging big data to uncover important processes in the development and progression of complex diseases.