Bio

Josh is a PhD student at Cambridge predominantly working on Bayesian approaches to physical layer problems in optical communications networks. Coming from a physics background, Josh is particularly interested in how prior knowledge obtained from physical models can be used to enhance the performance and explainability of machine learning approaches. He is looking for collaborations in application areas in which approximate physical models exist and domains which require a more explainable, probabilistic approach to machine learning.