I’m interested in how to learn models and make inferences given evidence from high-throughput biological datasets. The models that we develop range from mechanistic differential equation models of the cell to more abstract probabilistic machine learning models that can be used uncover interesting structure in high-dimensional data. I’m particularly interested in hybrid models that combine aspects of mechanistic and probabilistic models.

Models encode our hypotheses about how biological systems work. We use probabilistic inference to learn the model parameters and to choose between competing models so as to identify the hypotheses best supported by the available experimental evidence. Bayesian inference and non-parametric modelling is a particular focus as this provides a principled framework for dealing with uncertainty in complex systems.

I’m also Director of the Manchester ELLIS Unit which fosters collaboration with AI researchers across Europe and from May 2026 I also have a position at the CRUK Manchester Institute.