Mathematics, Statistics and Physics - Mathematics, Statistics and Physics

My main research interests (in no particular order):

Multivariate diffusion processes
Stochastic kinetic models represented by jump processes
Efficient simulation based Bayesian inference (using e.g. MCMC, SMC, pMCMC) for large stochastic models with particular focus on diffusion and jump processes
Sampling conditioned jump and diffusion processes
Emulation of computer models via Gaussian processes

Scalable sequential inference schemes for complex epidemic models - Sam Whitaker, EPSRC funded PhD student (2019-22), jointly supervised with Dr Colin Gillespie
Streaming data modelling for realtime monitoring and forecasting, funded by The Alan Turing Institute (2019-20), with Prof. Darren Wilkinson and Dr Sarah Heaps
Efficient parameter estimation for quantitative systems pharmacology, EPSRC IAA and AstraZenica Ltd. funded (02/19-04/19), with Dr Colin Gillespie
Variational inference for SDEs - Tom Ryder, CDT funded PhD student (2017-20), jointly supervised with Dr Dennis Prangle
Accelerating pseudo-marginal Metropolis-Hastings schemes for partially observed Markov process models - Tom Lowe, EPSRC funded PhD student (2017-20), jointly supervised with Dr Colin Gillespie
Assessing the effect of caloric restriction on core temperature and physical activity in mice using stochastic differential equation driven state space models - Ashleigh McLean, CDT funded PhD student (2016-19)
Fundamentals of Hamiltonian Monte Carlo for Bayesian inference of phylogenetic trees - Matthew Robinson, EPSRC funded PhD student (2015-18), jointly supervised with Dr Tom Nye and Prof Richard Boys

Bayesian calibration of stochastic kinetic models using spatial Dirichlet processes - Aamir Khan, EPSRC funded PhD student (2014-17), jointly supervised with Prof Richard Boys
Urban sustainability through Data Analytics - Yingying Lai, SAgE Faculty funded PhD student (2014-18), jointly supervised with Prof Richard Boys and Prof Phil Taylor (Newcastle Institute for Research on Sustainability)
Bayesian inference for stochastic differential mixed-effects models - Gavin Whitaker, PhD student (2011- June 2014, July 2015-2016), jointly supervised with Prof Richard Boys
Mathematical models for the developed Neolithic - funded by Leverhulme Trust (2009-12), with Dr Graeme Sarson, Prof Anvar Shukurov, Prof Richard Boys, Dr Andrew Baggaley and Dr Daniel Henderson

Joel Groves: Stochastic differential equations in finance, 2019
Emma Bradley: Pseudo-marginal MH schemes for diffusion processes, 2016
Tim English: Sequential Monte Carlo for stochastic kinetic models, 2016
Andrew Robson: Pseudo-marginal MCMC for diffusion processes, 2015
Josh Rushton-Crawshaw: Metropolis adjusted Langevin schemes for a linear class of diffusion processes, 2015
Matthew Upton: Sequential Monte Carlo schemes for Markov jump processes, 2014
Aleksandra Svalova: Bayesian inference for stochastic differential equations with application to finance, 2013
Elizabeth Goodall: MCMC for stochastic kinetic models via a linear noise approximation, 2012
Lane Stephenson: Bayesian nonparametric regression using Gaussian processes, 2011

Rachel Binks: Bayesian inference for low-order models of the geodynamo, 2018
Tom Lowe: Stochastic differential equation models of the geodynamo, 2017
Matheus Sebestyen Paiola: Monte Carlo pricing of options, 2015
John Duke: Numerical simulation and inference for stochastic differential equation models in systems biology, 2010 (with Prof Richard Boys)
Lauren Speight: Approximate Bayesian computation for stochastic kinetic models, 2010 (with Prof Richard Boys)
Rochelle Firth and Susanna Roig: Stochastic simulation for systems biology models, 2007 (with Dr Colin Gillespie)

Dr Andrew Golightly,
School of Mathematics, Statistics and Physics
Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Telephone: +44 (0)191 208 7312, Fax: +44 (0)191 208 8020
E-mail: a.golightly at ncl.ac.uk