Professor Geoff Nicholls

Associate Professor of Statistics

Biographical Sketch

Professor Geoff Nicholls, B.Sc. (Physics, Canterbury, New Zealand), MA, PhD (HEP, Cambridge, UK), teaches probability, statistics and applied mathematics. Geoff Nicholls joined the Statistics Department in 2005 from the Mathematics Department of the University of Auckland in New Zealand. Geoff took his BSc at the Physics Department of the University of Canterbury in Christchurch, New Zealand, and his PhD at Clare College, Cambridge, where he studied particle physics in the Department of Applied Mathematics and Theoretical Physics.

Research Interests

  • Bayesian inference
  • Statistical methods
  • Computational statistics
  • Monte Carlo, statistical senetics
  • Applied statistics

Geoff is working on Monte-Carlo based Bayesian statistical inference for problems with computationally demanding prior and likelihood evaluations. Practical computational methods for making Bayesian model comparison for complex stochastic systems are needed. Research is driven by problems from a range of application areas, including Geoscience, Linguistics, Genetics and Archaeology.



Wu, C.-H., Roeder, A. and Nicholls, G. (2023) “Biclustering random matrix partitions with an application to classification of forensic body fluids.”
Jiang, C., Nicholls, G. and Lee, J. (2023) “Bayesian Inference for Vertex-Series-Parallel Partial Orders”, in Proceedings of Machine Learning Research, pp. 995–1004.
Nicholls, G., Lee, J., Karn, N., Johnson, D., Huang, R. and Muir-Watt, A. (2022) “Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta.”
Carmona, C. and Nicholls, G. (2022) “Scalable Semi-Modular Inference with Variational Meta-Posteriors.”
Nicholls, G., Lee, J., Wu, C.-H. and Carmona, C. (2022) “Valid belief updates for prequentially additive loss functions arising in Semi-Modular Inference.”
Carmona, C. and Nicholls, G. (2021) “Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components”, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics. Proceedings of the Machine Learning Research, pp. 4226–4235.

Contact Details

College affiliation: Fellow and Tutor at St Peter's College

Office: 1.12 

Graduate Students