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.
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.
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.
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.