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 the Dark Energy Survey, with machine learning for feedback-based models of galaxy evolution through dark matter halos in the context of cosmological hydrodynamical and N-body simulations, and with novel statistical methods to explore dark energy and cosmological models. In addition, my research covers computational finance and spatial optimization problems in criminology. Below is a (rough) list of my research interests and visits:

Cosmology and astrophysics:

  • Dark energy and large-scale structure
  • Cosmological parameter estimation
  • Galaxy formation and evolution

Statistics / machine learning:

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

Finance, OR and other fields:

  • FinTech and market microstructure
  • Portfolio and risk optimization
  • Quantitative criminology

Research visits and programs: