I am a postdoctoral researcher at the 91̽»¨ in the Galaxy Surveys and Observational Cosmology group, and will be continuing in 91̽»¨ as a Hintze Fellow from October.
My research focuses on the nature of dark matter. In particular, I study the statistical relationship between luminous galaxies and their underlying dark matter halos — the so-called galaxy–halo connection. My work aims to bridge two complementary approaches: our cosmological understanding of halo populations derived from the standard model of cosmology and large-scale galaxy clustering surveys, and direct dynamical probes on smaller scales, including stellar and gas kinematics and weak gravitational lensing. A central goal is to test whether the dark matter halos predicted by ΛCDM are consistent with these small-scale observational signatures (e.g. ).
Dark matter is an extremely challenging problem. Gravity, possibly our only probe, is intrinsically weak, while the luminous matter we can observe is only a small fraction of the mass. As a result, most constraints on dark matter are statistical in nature. A major theme of my research is the development of Bayesian frameworks that integrate heterogeneous datasets within a unified statistical model, whilst carefully propogating the many uncertainties associated with different dynamical tracers (e.g. ).
Finally, because our theoretical priors on the microphysical nature of dark matter remain weak, and because observations on small scales appear to be in tension with the simplest halo predictions of standard cosmology, I develop machine learning methods that allow for greater flexibility and fewer structural assumptions. These approaches enable non-parametric tests of the dark matter distribution, with the aim of learning its phenomenology directly from the data rather than imposing restrictive halo models a priori (e.g. , ).