Statistical machine learning, data analysis and cosmology
I primarily work on the understanding and methodology of statistical machine learning for use in robust, scientific data analysis. Particularly, I focus on the use of Bayesian tools and novel computational techniques to learn about our universe from cosmological and astronomical datasets. I also have interests and experience in using stastistical and computational techniques for solving problems in domains outside of cosmology and astrophysics.

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.

I am available for consultancy to both industry and academia in matters concerning machine learning, deep learning, interpretability and statistical methods.
Address
Bureau 62a, Institut d'Astrophysique de Paris
98 bis boulevard Arago
Paris
75014
France

email: tom@charnock.fr
Tom Charnock's Twitter Feed