Associate Professor of Statistics, University of Oxford
Tutor in Statistics and Tutor for Welfare, 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.
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].
Publications :
Oxford CSML 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
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.