Alexander Mead
Machine Learning | Data Science | Astrophysics

Alexander Mead

Data Science

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.


You can email me at

My data science CV can be found here and my academic CV can be found here.

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