About us

The areas of research of the Machine Learning and Statistics Methodology (MLSM) research group include the development of new methods and software motivated by applications in ecology, epidemiology, biostatistics, finance and genetics. The group works closely with their colleagues within the Statistical Ecology (part of the Centre for Research into Ecological and Environmental Modelling (CREEM)) and Statistical Medicine and Molecular Biology research groups.

Research areas

Machine learning:
deep learning methods for image, audio, and genetic processing with applications in medicine and ecology
(contact Dr Chrissy Fell for more information)

Bayesian inference:
elicitation of prior knowledge, model selection and model-averaging, efficient and generic reversible jump Markov chain Monte Carlo algorithms, application of sequential Monte Carlo methods to estimation in state-space models
(contact Dr Michail Papathomas for more information)

methods for integrated modelling of diverse data sources (e.g. genome mapping, genetic, microarray, and proteomics data), methods and software to enable biological inference.
(contact Prof Andy Lynch for more information)

Design of experiments:
block designs, randomization, multi-tiered experiments
(contact Prof. Rosemary Bailey for more information)

Estimation of population size:
capture-recapture methods, distance sampling
(contact Prof. David Borchers for more information)

Model fitting techniques:
sequential Monte Carlo methods (such as particle filters), Markov chain Monte Carol
(contact Prof Len Thomas for more information)

Smoothing methods:
spatially adaptive smoothing in one and two dimensions, P-splines
(contact Prof Monique MacKenzie for more information)

Causal inference:
target trials and spatio-temporal inference
(contact Dr Ben Swallow or Dr Benjamin Baer for more information)

Statistical genetics:
association mapping methods, fine-scale mapping, heterogeneous populations, inbred populations, genome-wide data
(contact Dr Michail Papathomas for more information)

Analysis of clustered and censored data:
Bayesian inference for censored data, model-based clustering, mixture of experts modelling, censored data analysis and survival analysis.
(contact Dr Elham Mirfarah for more information)