Professor Murray Pollock (Formerly Department of Statistics at Warwick, now based in the School of Mathematics, Statistics and Physics at Newcastle University)
A surprisingly challenging problem in computational statistics is how to unify distributed statistical analyses and inferences into a single coherent inference.
This problem arises in many settings (for instance, combining experts in expert elicitation, incorporating disparate inference in multi-view learning, and recombining in distributed big data problems), but a general framework for conducting such unification has only recently been addressed.
A particularly compelling application is in statistical cryptography. Consider the setting in which multiple (potentially untrusted) parties wish to share distributional information (for instance in insurance, banking and social media settings), but wish to ensure information theoretic security. Joint work with Louis Aslett, Hongsheng Dai and Gareth Roberts.