Prof Geoff Nicholls [photo]

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

I joined the CSML group in 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 : Oxford CSML and Google Scholar

Keywords: Bayesian inference, approximation methods; misspecified models;
Ranking and partial orders; Monte Carlo methods and calibration.

Current Research Students - DPhi:
Laura Battaglia Bayesian Statistical Methods and Computation
Chris Carmona Model mispecification and big missing data
Jessie Jiang Statistical inference for partial orders
Schyan Zafar Non-parametric stochastic processes and time-evolving word meanings

Recent Graduates - DPhil:
Lorenzo Pacchiardi (submitted, 2022) Statistical inference in generative models using scoring rules
Hanwen Xing (submitted, 2022) Diagnostic Methods for Bayesian Inference
Luke Kelly (2016) A stochastic Dollo model for lateral transfer: phylogenetics and lexical traits
Ross Haines (2016) Estimation of spatial fields and measurement locations: the Atlas of Late Middle English
Alexis Muir-Watt (2016) Inferring partial orders from random linear extensions: models for social hierarchy

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, and lately,
a Part C and MSc course in Bayes Methods.

Here is a pointer to these Department Courses. I also supervise Part C and MSc dissertations.

Recent Graduate Projects - MSc in Statistical Science, and MMath and MMathStats Part C
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.