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.

Publications

Roeling, M. and Nicholls, G. (2020) “Imputation of attributes in networked data using Bayesian autocorrelation regression models”, Social Networks, 62, pp. 24–32.

Carmona, C. and Nicholls, G. (2020) “Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components”, in Proceedings of Machine Learning Research, pp. 4226–4235.

Xing, H., Nicholls, G. and Lee, J. (2020) “Distortion estimates for approximate Bayesian inference”, in Proceedings of Machine Learning Research, pp. 1208–1217.

Xing, H., Nicholls, G. and Lee, J. (2020) “Distortion estimates for approximate Bayesian inference”, in CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020), pp. 1208–1217.

Cui, T., Fox, C., Nicholls, G. and O’Sullivan, M. (2019) “Using parallel Markov chain Monte Carlo to quantify uncertainties in geothermal reservoir calibration”, International Journal for Uncertainty Quantification, 9(3), pp. 295–310.

Xing, H., Nicholls, G. and Lee, J. (2019) “Calibrated approximate Bayesian inference”, in Proceedings of Machine Learning Research. Journal of Machine Learning Research, pp. 6912–6920.

Rodrigues, E., Nicholls, G., Tarumoto, M. and Tzintzun, G. (2019) “Using a non-homogeneous Poisson model with spatial anisotropy and change-points to study air pollution data”, ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 26(2), pp. 153–184.

Moores, M., Nicholls, G., Pettitt, A. and Mengersen, K. (2018) “Scalable Bayesian inference for the inverse temperature of a hidden Potts model”, Bayesian Analysis [Preprint].

Kelly, L. and Nicholls, G. (2017) “Lateral transfer in Stochastic Dollo models”, Annals of Applied Statistics, 11(2), pp. 1146–1168.

Styring, A., Charles, M., Fantone, F., Hald, M., McMahon, A., Meadow, R., Nicholls, G., Patel, A., Pitre, M., Smith, A., Sołtysiak, A., Stein, G., Weber, J., Weiss, H. and Bogaard, A. (2017) “Isotope evidence for agricultural extensification reveals how the world’s first cities were fed”, Nature Plants, 3, p. 17076.