My dynamical modelling research is dominated by the study of the Milky Way, and how best to interpret and exploit the enormously rich but confusing data we are gaining from numerous large surveys, especially Gaia.
In particular I'm focused on producing dynamical models which can be compared to observations of the Milky Way, as it's only through understanding the Galaxy's dynamics that we will be able to discover anything about its dark matter content, or infer anything about the regions of the Galaxy that we can't survey from those which we can.
I work primarily on methods which exploit action-angle coordinates. A major focus is "torus mapping" which operates on an orbit-by-orbit basis, and is the basis for sophisticated chemodynamical models. I also work with approximations that allow other forms of calculations, such as determining the Milky Way potential from survey data").
Additionally I have dedicated significant work to interpreting simpler datasets in an effort to establish our current level of knowledge about the Milky Way. This includes my widely used graviational potential models, from my highly cited papers "The mass distribution and gravitational potential of the Milky Way (2017) " , and "Mass models of the Milky Way (2011)" which attempt to distill our current understanding of the Galaxy's structure into a single model.
I am abuilder for the RAVE survey, reflecting my work on the survey infrastructure. I am in charge of the pipeline that determines the distance, ages and stellar parameters for stars from RAVE observations, using Bayesian techniques to include information from photometric surveys and Gaia parallaxes.
For the Gaia data processing consortium, I was a lead on author on a science demonstration paper which looked at the dwarf galaxies around the Milky Way in Gaia DR2. I am also involved with the astrometry team, determining the correlations between output parameters from Gaia data analysis. This follows the thesis work on this topic by Berry Holl