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People » Academic and research

This page lists the main research interests of Department staff. For more detailed information, follow the links to individual home pages.

Jump to: Faculty · Research Fellows · Emeritus/Honorary · Associates

Academic staff (Faculty):



Research Fellows:

Dr Jake Carson rsz_1jake.jpg Exact inference for Partial Differential Equations
Mr Kevin Engelbrecht rsz_kpe.jpg Stochastic control theory and BSDEs to the pricing and hedging of life insurance contracts
Dr Daphne Ezer rsz_ezer.png Network inference algorithms and computer-aided experimental design
Dr Elena Hernandez
-Hernandez
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Stochastic control in continuous time, and fractional differential equations and their connection with probability theory

Dr Ed Knock    
Dr Mateusz Majka rsz_majka_photo_s.jpg Stochastic differential equations, Levy processes, Monte Carlo methods
Dr Linda Nichols
(AS&RU)
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Statistical analysis of large observational cohort studies and clinical trials and interested in the use of electronic patient records for research.

Dr Renata Retkute rsz_1573064.jpg A fine-scale spatial dynamics and Bayesian inference for Neglected Tropical Diseases and Avian Influenza
Dr Jim Skinner rsz_academic-photo.jpg Machine learning algorithms
Dr Nick Tawn rsz_nicholas_tawn.jpg

Computational Statistics

Dr Panayiota Touloupou rsz_panayiota.jpg Bayesian inference and model selection for partially observed stochastic epidemics


Emeritus Professors:

Professor John Copas Statistical modelling and inference. Models for censoring and selection bias. Local likelihood. Meta-analysis. Applications, particularly in medicine and criminology.
Professor Tony Lawrance Statistical analysis and nonlinear modelling of financial time series.


Honorary Professor:

Dr Tim Davis Honorary Professor
Professor John Fox Honorary Professor
Professor Thomas Nichols Honorary Professor
Dr David Rossell Honorary Associate Professor



Associate Fellows:

Dr David Croydon Probability theory
Dr John Fenlon
Dr Heather Turner Statistical modelling and statistical programming using the open source software R