Alan Saul
Research Scientist at Secondmind
Oxford, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolAlan Saul is a Research Scientist based in Oxford with 13 years of experience developing and deploying Bayesian probabilistic machine learning for industrial problems. Currently at Secondmind, he specializes in Gaussian processes and probabilistic methods that bridge academic rigor with production constraints. His PhD work at Sheffield under Neil Lawrence and sustained open-source contributions to widely used repos like GPy and GPyOpt reflect deep expertise in kernels, predictive densities, and Bayesian optimization. Alan combines hands-on engineering—fixing optimizer bugs, expanding test suites, and improving core model code—with a practical focus on real-world data complexity. Colleagues rely on him to translate advanced probabilistic modeling into robust, maintainable systems that deliver business value.
13 years of coding experience
8 years of employment as a software developer
Master's degree & BSc degree, Computer Science, First-class honours, average 83%, top 1%, Master's degree & BSc degree, Computer Science, First-class honours, average 83%, top 1% at The University of Sheffield
Stackoverflow
23reputation
2kreached
0answers
1question
Github Skills (27)
unit-testing10
python10
testing10
mathematics10
machine-learning10
mathematical10
math10
numpy10
gaussian-processes10
bayesian10
statistical-models10
optimisation10
unit-test10
modeling10
optimization10
Programming languages (5)
Github contributions (5)
SheffieldML/GPy
Jan 2013 - Nov 2017
Gaussian processes framework in python
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:622 commits, 9 PRs, 78 pushes in 4 years 10 months
Contributions summary:Alan primarily contributed to improvements within the GPy library, a Gaussian Process framework in Python. The user's contributions included implementing code for various kernel operations, such as calculating covariance matrices. They also worked on integrating quadrature methods and predictive density calculations, particularly for the student-t likelihood. Additionally, the user made updates to example scripts showcasing different aspects of Gaussian process regression and data visualization.
gaussian-processespython
SheffieldML/GPyOpt
Jun 2016 - Dec 2017
Gaussian Process Optimization using GPy
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 1 PR, 3 pushes in 1 year 6 months
Contributions summary:Alan contributed to the GPyOpt repository by addressing backward compatibility issues, fixing bugs related to optimizer kwargs, updating and expanding existing test suites, and correcting a notebook error. Their work primarily involved modifying the core components of the Bayesian optimization library, including model definitions, acquisition optimizers, and testing frameworks. Furthermore, the user also addressed minor code quality issues and added a checkpoint to the gitignore file.
gaussian-processesgpy
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