My research interests are in the statistical design of experiments (DOE), network science, and how the two areas interact.
The statistical design of experiments allows scientists to maximise the amount of information they get from experiments. It has been used traditionally in agriculture, and heavy industry, to enable scientists to optimise the scientific method- how can we do the best experiments, sometimes at the lowest cost. This area of research is well established.
In our recent paper,we looked at experiments when experimental units were connected according to some network relationship. We showed how these networks could be useful in a variety of experimental applications: for example, agricultural experiments where experimental units (plots) were connected by some spatial relationship, and also in crossover trials, where experimental units were connected by temporal networks.
In current work, we argue that there is a wide class of experiments that can be reformulated into a problem of design on a network, and that by presenting the problem in this networked form, we can develop faster new algorithms that allow optimal designs on the original problems to be found more quickly. In other words, by regarding experimental design models as problems in network science, we can improve experimental design algorithms even when there is no obvious network relationship.