Alexander Mead
Software Engineering | Astrophysics | Machine Learning
Software Engineering
I currently work as a software engineer at digiLab a deep-tech startup that specialises in the quantification of uncertainty. I mainly develop the twinLab software, a general-purpose machine-learning platform that is able to train AI models that quantify the uncertainty in their predictions. I work on the full stack, from the various customer-facing front ends to the serverless cloud infrastructure that trains and runs the models. I oversee the scientific programming required for the library; adding new features and updating algorithms to improve performance. I also contribute to and run data-science projects for clients in safety-critical industries where quantified uncertainty is paramount.
Prior to working at digiLab I worked at the University of British Columbia as part of the Programming Languages for Artificial Intelligence (PLAI) group. I worked at the intersection of probabilistic programming and deep learning, helping to develop new techniques for solving inference problems with general simulation-specified forward models.
Astrophysics
As an astrophysicist, I was 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 have worked on 'rescaling' methods to alter the cosmology of an existing simulation by remapping length and time units and modifying the internal structure of dark-matter 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 provides non-linear spectra rapidly at high accuracy. HMcode is also incorporated within CAMB.
I also used deep learning to infer orbital parameters in Kepler planetary systems with strong transit-time variations. These occur when massive planets have relatively close orbits, such that the orbits interact with each other and cause strong deviations from the Keplerian solution.
Selected academic publications
HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback
Mead, Brieden, Troester, Heymans
MNRAS, 2021
Spherical collapse, formation hysteresis and the deeply non-linear cosmological power spectrum
Mead
MNRAS, 2017
Mead, Heymans, Lombriser, Peacock, Steele, Winther
MNRAS, 2016
A full list of my academic publications can be found here.
Contact
You can email me at alexander.j.mead@googlemail.com
My professional CV can be found here and my academic CV can be found here.