Prof Geoff Nicholls [photo]

Associate Professor of Statistics, University of Oxford
Tutor in Statistics and Tutor for Welfare, St Peter's College Oxford

I joined the Statistics Department here in Oxford in 2005 from the Math department in Auckland
in New Zealand. I was HOD here from 2012-2015. I have a college homepage at St Peters.

Contact details

Address: Department of Statistics, 24-29 St Giles, Oxford, OX1 3LB, UK
Phone: Dept +44-1865-282853; College +44-1865-278938
Email: nicholls@stats.ox.ac.uk
Office: Room 1.12
Maps: [Dept/College], [Streetview].

Research

Publications : On my department homepage and Google Scholar

Keywords: Bayesian inference, approximation methods and calibration; misspecified models
and Semi-Modular Inference (SMI); Ranking and partial orders; Monte Carlo methods. Biclustering.

Current Research Students - DPhil:
Sitong Liu Generalised Bayesian inference
Laura Battaglia Bayes methods for misspecified models
Holly Li Scalable Statistical inference for partial orders
Shiyi Sun New variational methods for Bayesian inference

Recent Graduates - DPhil:

Jessie Jiang (2024) Statistical inference for partial orders
Schyan Zafar (2024) Bayesian inference for multivariate time series
Chris Carmona (2023) Bayesian Semi-Modular Inference
Lorenzo Pacchiardi (2022) Statistical inference in generative models using scoring rules
Hanwen Xing (2022) Diagnostic Methods for Bayesian Inference

Teaching and Supervision

I tutor Statistics and Probability and some Applied Math in College. In the Statistics Department
I teach part of a module on Bayesian Inference in the StatML Doctoral training CDT, a Part C
and MSc course in Bayes Methods and the MSc Case Studies course (a journal club). Here is a pointer
to these Department Courses.

 

Here are the 2025 lecture notes for SC7 Bayes Methods. Previous versions are ghastly.

This version is… less horrible. Exercises are here.

 

I also supervise Part C and MSc dissertations.

Recent Graduate Projects - MSc in Statistical Science, and MMath and MMathStats Part C
Maksymilian Dac, Cetian Liu, Jonathan Oko and Jiaqi Cao (2025) Evaluation and Analysis of new Models for Rank Data
Kristyna Coufalova (2024) Models for time-varying partial rank data
Emil Javurek (2024) New methods for variational Bayesian inference
Ella Warde, Pablo Fernandez Del Amo and Štěpán Hartman (2024) Scalable Models for Rank Data
Florian Wittstock (2023) Target-Aware Amortized Ratio Importance Sampling
Andrew Challenger (2023) Time-series models for context dependent ranking
Yuqi Zhang, Zackary Allinson, Abhinav Mukherjee and Barney Hong (2023) Models for Rank Data
Qinyu Li (2022) Modifications to Bayesian Inference under Model Misspecification
Xinan Xu (2022) Estimation of Marginal Likelihoods and Bayes Factors
Alexander Barry (2022) Generalised Bayes
Weiting Yi (2022) An Enhanced Generalised Emulation Framework for the Lorenz-96 Inverse Problem
Xiangyu Wu (2021) Monte Carlo methods for the parameters of a Strauss Process likelihood
Varsha Ramineni (2021) Robust Bayesian inference for Indo-European lexical trait evolution
Cameron Bell, Dennis Christensen, Andrei Crisan, Joshua King (2021) Topics in Contrastive Learning
Alex Sauer (2020) Contrastive Learning and related likelihood-free methods for statistical inference
Olle Tieljooij (2020) Deep-learning statistical models for investment factors
Juha Kreula (2020) Spatio-temporal species distributions and presence-only data
Chunyi Luo, Hongyu Qian, Sam Field and Yuyang Shi (2020) Component-wise ABC and Random forest ABC

Here are the term dates for the next few years.