I am an S2DS fellow looking for new challenges as a Data Scientist. I have extensive experience with: Python (inc. numpy, scipy, pandas, scikit-learn); C; Fortran; bash. From my career in astrophysics I have advanced knowledge of: statistics; Fourier techniques; Markov chain Monte Carlo (MCMC); numerical equation solving. I have real-world experience working for Thymia where I was part of a team that developed a machine-learning pipeline to assess the mental health of app users. In my career as an astrophysicist I have a track record of managing projects to completion and publishing quality academic papers. I worked individually, and as a part of a team, to perform cutting-edge research at the frontiers of cosmology.
As an astrophysicist, I am interested in how non-linear cosmological structure formation can be understood using inspiration from N-body simulations. These simulations are extremely useful, but are too expensive to be run for every cosmological scenario under consideration. I worked on 'rescaling' methods to alter the cosmology of an existing simulation by remapping length and time units and modifying the internal structure of haloes. I also developed an augmented version of the halo model to produce accurate non-linear matter power spectra, which are useful for analysing weak-lensing data. This code is publicly available HMcode and rapidly provides non-linear spectra at high accuracy. HMcode is also incorporated within CAMB.
Mead, Brieden, Troester, Heymans
Mead, Heymans, Lombriser, Peacock, Steele, Winther
A full list of my academic publications can be found here.