|Prof Darren Wilkinson||Professor of Stochastic Modelling|
|School of Mathematics, Statistics and Physics|
Although my background and particular expertise are both in Bayesian statistical inference, I have a broad range of research interests, cutting across mathematics, statistics, probability theory, modelling, computing science, and molecular and systems biology.
Most of my current research interests involve applications of Bayesian statistics to a variety of challenging problems in Molecular biology, Bioinformatics and Systems Biology. I am especially interested in parameter inference for dynamic biological models, and the use of approximate models and (stochastic) model emulators for rendering computationally prohibitive algorithms for "big data" more tractable. I'm also interested in the way that HPC and Cloud computing technology can help, guided by principles of Functional Programming, especially in the context of streaming data modelling. My research blog give some insight into my current interests.
Bayesian analysis, scalable Bayesian computation, and Bayesian software development; Dynamic and graphical models, and Markov processes; Statistical bioinformatics and stochastic models in computational systems biology; Functional programming, Cloud computing and e-Science technology for computational statistics and big data; Efficient Bayesian algorithms and Bayes linear methods.
I held a BBSRC Research Development Fellowship to investigate Integrative modelling of stochasticity, noise, heterogeneity and measurement error in the study of model biological systems, and this topic remains a major focus of my research. The project involved a combination of statistical modelling and experimental lab work exploiting model organisms Bacillus subtilis (a gram-positive bacterium) and Saccharomyces cerevisiae (budding yeast). I am still working closely with the Bacillus lab of Leendert Hamoen and the yeast lab of David Lydall.
The rationale for undertaking this project can be found in my recent Nature Reviews Genetics article: Stochastic modelling for quantitative description of heterogeneous biological systems.