Staff Profile
Dr Dani Leonard
Lecturer in Data Science
My research applies statistical and machine learning methods to data. My primary application area is in observational cosmology, where I use Bayesian parameter inference and model comparison techniques to constrain and compare cosmological models. Within this area, I have two main scientific focuses: astrophysical modelling uncertainties affecting weak gravitational lensing and galaxy clustering, and exploring the possibility of cosmological deviations from General Relativity. More generally, I am interested in methods for sampling and inference in high-dimensional spaces, approximate modelling to accelerate inference within complex physical systems, and methods for mitigating selection and model-misspecification biases.
Check out my website . A list of my publications can also be found .
In 2025-2026, I am teaching:
- PHY3042, Cosmology
- Supervising on MAS3094 (Group Project)