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.
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
- 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 (2022) Statistical
inference in generative models using scoring rules
Hanwen Xing (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
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.
Here are the lecture notes for SC7 Bayes Methods. Previous version 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
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.