2019: Professor Alexei Drummond, University of Auckland has been awarded a James Cook Research Fellowship in Physical Sciences for research titled ‘Integrative, efficient, and computationally reproducible scientific software for Bayesian inference in phylogenetics, population genetics and molecular evolution’
The fields of scientific computing and data science have both expanded exponentially in the last decade with the availability of vastly more data than at any point in the past. With this flood of data and the proliferation of associated computational models and tools, it has become difficult for users to accurately understand and efficiently communicate the models they are using. This leads to problems with efficiency and reproducibility. Researchers have begun to face computational limitations due to a rapid increase in the size of typical data sets. Recent contributions in scientific computing have promised a paradigm shift towards ‘computationally reproducible’ science. The motivation for this shift is the desire to close the gap between performing scientific analyses and communicating the methods used. This would result in improvements to productivity, transparency and reproducibility. During his James Cook Research Fellowship Professor Drummond aims to address these and related issues in the context of population genetics and molecular evolution.
Over the years, Professor Drummond has developed novel statistical models to understand complex biological problems such as molecular evolution, population genetics, and infectious disease modelling, notably the popular software ‘BEAST’. BEAST has given evidential weight to new theories in biology and informed public health policy and conservation management. BEAST modelling has challenged conventional wisdom and led to a considerable change in modelling biological problems. By embracing ideas at the forefront of modern data science and scientific computing, Professor Drummond will work to develop the next paradigm for scientific computing in the domain of statistical inference for complex biological problems through a radical upgrade and modification of the underlying algorithms and programming languages underpinning BEAST. He aims to produce a concise, computer and human readable modelling language that can naturally describe rich combinations of common molecular evolutionary models. Professor Drummond will also develop a high-level browser-based programmable user-interface and integrate the developed software with existing tools for computational reproducibility. This programme of research will result in a major advance in computational methods and tools for evolutionary analysis.