Research


My research interests are mostly centered on the intersection of cosmology, astrostatistics and machine learning. I’m part of the Institute for Astronomy at the Royal Observatory, Edinburgh, where my current projects deal primarily with Bayesian nonparametrics for parallelized and high-dimensional cosmological parameter estimation for DES, and with deep learning for feedback-based models of galaxy evolution with the MUFASA and SIMBA suites of cosmological hydrodynamic simulations. Below is a (rough) list of my main research interests and visits:

Cosmology and astrophysics:

  • Cosmological parameter estimation
  • Galaxy formation and evolution
  • Constraints on dark energy

Statistics and machine learning:

  • Non-parametric Bayesian methods
  • Estimation theory and sampling
  • Deep learning techniques

Research visits and programs: