Statistical cosmology and machine learning
I primarily work on the statistical analysis of cosmology and cosmological data using Bayesian tools and machine learning.

Particular topics I am interested and working in are: quantifying discrepancies between datasets for given models to determine whether model extensions are needed; using deep learning to analyse data and solve statistical problems; and calculating the effects of exotic physics on the CMB.

I am currently a CNRS funded post-doctoral researcher at the Institut d'Astrophysique de Paris working on the BIG4 program (big datasets, big simulations, big bang, big problems: algorithms of Bayesian reconstruction constrained by physics, application to cosmological data analysis). Previously, I studyied for my PhD in cosmology at the Particle theory group at the University of Nottingham, after graduating in 2013 from the University of Nottingham with an MSci (Hons), First Class, in theoretical physics.
Bureau 62a, Institut d'Astrophysique de Paris
98 bis boulevard Arago

Tom Charnock's Twitter Feed