The Royal Statistical Society has awarded the Guy Medal in Silver to Christ’s Fellow, Professor Mark Girolami.
The silver medal is awarded annually for outstanding contribution to the development of statistics. Guy Medals are named after William Augustus Guy, the nineteenth-century British medical statistician.
Professor Girolami is Chief Scientist at The Alan Turing Institute, Sir Kirby Laing Professor of Civil Engineering and Chair of the Royal Academy of Engineering Research.
Professor Girolami said:
“I am truly honoured to receive the Guy Medal in Silver from the Royal Statistical Society. It is an even greater honour to be placed in such distinguished ranks as those of the past honourees, all of whom have made important and innovative contributions to the theory and application of Statistical Science.”
Professor Girolami will be presented with the award at a ceremony during the Society’s annual conference in Harrogate this September.
The official citation said:
“The Guy Medal in Silver is awarded to Mark Girolami for his substantial contributions to computational statistics and machine learning, in particular, his work on differential geometric approaches to stochastic simulation for statistical inference published in the paper, “Riemann manifold Langevin and Hamiltonian Monte Carlo methods” (with co-author Calderhead), which was read to the Society in 2011. The paper attracted one of the largest number of contributions to the discussion of any paper ever presented in the history of the Society including one by Sir David Cox FRS.
He has published widely on many fundamental and challenging topics, such as the methodological development of Monte Carlo based algorithms and is well known for pioneering a fundamental connection between information geometry and Markov chain Monte Carlo (MCMC), leading to new and powerful computational methods for intractable Bayesian inference problems. Further influential contributions include his work at the interface of statistics and cellular biology, the development of a theoretical underpinning to Deep Gaussian Learning as well as an approach to solving nonlinear differential equations and characterising numerical errors using probabilistic calculus, to name but a few.”