Professor Geoff Nicholls

About

Prof Nicholls teaches probability, statistics and applied mathematics. He is interested in developing new methodology and algorithms for statistical inference. He provides mathematical support for scientific research in a wide range of fields.

Since 2020, he has developed new methodology in statistics and AI, ranging from foundational work in mathematical statistics through to methods and software supporting continuous learning for AI-agents. His applied work is motivated by ongoing collaboration with researchers in archaeology, climate and environmetrics, human social history and animal dominance hierarchies, computational linguistics and philology. This kind of diversity is common for applied statisticians and is one of the joys of working in applied mathematics and statistics.

Selected publications

Jiang, C and Nicholls, GK, “Non-Parametric Bayesian Inference for Partial Orders with Ties from Rank Data observed with Mallows Noise”, Computational Statistics & Data Analysis, 224:108445 (2026)

Sun, S, Nicholls, GK and Lee, JE, “Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation”, Proceedings of the 43rd International Conference on Machine Learning (ICML), (2026)

Li, D, Cheng, Z, Nicholls, GK and Kong, Q, “De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution”, Proceedings of the 43rd International Conference on Machine Learning (ICML), (2026)

Nicholls, GK, Lee, JE, Karn, N, Johnson, D, Huang, R and Muir-Watt, A, “Bayesian inference for partial orders from random linear extensions: Power relations from 12th century royal acta”, Annals of Applied Statistics, 19(2), 1663-1690, (2025)

Zafar, S and Nicholls, GK, “An embedded diachronic sense change model with a case study from ancient Greek”, Comp. Stats. & Data Analysis, 199:108011 (2024)

Jiang, C, Nicholls, GK and Lee, JE, “Bayesian inference for vertex-series-parallel partial orders”, Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 216:995-1004, (2023)

Hu, R, Nicholls, GK and Sejdinovic, D, “Large Scale Tensor Regression using Kernels and Variational Inference”, Machine Learning, 111, 2663-2713 (2022)

Styring, AK, Carmona, CU, Isaakidou, V, Karathanou, A, Nicholls, GK, Sarpaki, A and Bogaard, A, “Urban form and scale shaped the agroecology of early ‘cities’ in northern Mesopotamia, the Aegean and Central Europe”, Journal of Agrarian Change, 22(4):831-854, (2022)

Lee, JE and Nicholls, GK, “Tree based credible set estimation”, Statistics and Computing, 31:69, (2021)

Roeling, MP and Nicholls, GK, “Imputation of attributes in networked data using Bayesian Autocorrelation Regression Models”, Social Networks, 62:24-32 (2020)

Xing, H, Nicholls, GK and Lee, JE, “Distortion estimates for approximate Bayesian inference”, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1208-1217, (2020)

Carmona, CU and Nicholls, GK, “Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components”, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:4226-4235, (2020)