About me
I’m a machine learning engineer in Brighton, UK.
I build and own production prediction platforms end to end — model architecture through to the infrastructure that serves them, and the evaluation that decides whether a model is allowed near production at all. Recently that has meant forecasting for consumer sports products: constrained optimisation over predicted outcomes, fed by noisy third-party data against fixed deadlines, where prediction quality is the product.
The part I find most interesting is the part that isn’t modelling. Deciding what a model has to satisfy before it ships — time-based evaluation splits, domain validation metrics, checks for the ways a model can be quietly degenerate rather than loudly wrong — tends to matter more than the architecture, and gets written about less.
Research
My PhD, at the University of Sussex under Prof. Novi Quadrianto, was on fair representation learning that stays in the data domain rather than an uninterpretable latent space, so per-sample information can be inspected and explained to the people who have to act on it. You can read the thesis here.
That thread — making machine learning legible and operable by people who aren’t the person who built it — runs through most of what I do, research or otherwise.
- Discovering Fair Representations in the Data Domain, CVPR 2019
- Null-Sampling for Interpretable and Fair Representations, ECCV 2020
- An Algorithmic Framework for Positive Action, ACM EAAMO 2021
Open source
I wrote and maintain EthicML, an open-source benchmarking framework for fair machine learning: common loaders for the standard fairness datasets, a built-in train/validation/test protocol, pluggable models and metrics, and automatic utility–fairness trade-off plots. It started because the lab was sharing code by emailing snippets, and it ended up being used in teaching and behind the experiments in published papers.
Elsewhere
📫 Reach me at oliverthomas@outlook.com.
😄 Pronouns: he/him.
⚡ Fun fact: the dress was gold and white. There was no blue and black dress.