Jacob Stevenson
Principal Software Engineer at Microsoft
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJacob Stevenson is a Principal Software Engineer based in Cambridge with 14 years of experience applying machine learning to improve productivity across Microsoft Office 365. Trained as a computational statistical physicist (PhD, UC San Diego), he blends rigorous research instincts with production software skills in Python, C++, C# and SQL. At Microsoft he focuses on document discovery and recommendation systems, drawing on past academic work in complex systems and thermodynamic sampling. An active open-source contributor, he implemented SciPy’s basin-hopping global optimization and improved documentation and GP sampling in the Spearmint Bayesian optimization project—signals of both algorithmic depth and attention to maintainability. Colleagues value his ability to translate advanced probabilistic methods into robust, user-facing features.
14 years of coding experience
University of California, San DiegoBachelor of Arts (B.A.) Physics, Bachelor of Arts (B.A.) Physics at Cornell University
Github Skills (14)
algorithm10
algorithms10
machine-learning10
scipy10
bayesian10
optimisation10
python10
optimizers10
scientific-computing10
optimization10
gaussian-processes10
documentation9
matplotlib8
numpy8
Programming languages (7)
Github contributions (5)
scipy/scipy
Oct 2012 - Nov 2013
SciPy library main repository
Role in this project:
Backend Developer
Backend DeveloperContributions:4 reviews, 35 commits, 54 comments in 1 year 1 month
Contributions summary:Jacob implemented the basin-hopping global optimization algorithm within the SciPy library. Their contributions included adding the algorithm, writing the initial code, and performing a series of refactoring and cleanup steps to improve its functionality and maintainability. The user also added tests, documentation, and made various minor adjustments to the code. This work focused on enhancing the optimization capabilities of the SciPy library.
scipypythonscientific-computing
HIPS/Spearmint
Oct 2014 - Oct 2014
Spearmint Bayesian optimization codebase
Role in this project:ML Engineer
ML EngineerContributions:7 commits in 3 days
Contributions summary:Jacob contributed significantly to the codebase by adding extensive documentation and comments, improving the readability and maintainability of the Gaussian Process (GP) model implementation. They also modified the slice sampler and other utility functions related to parameter handling, suggesting involvement in the model fitting and sampling process. Furthermore, the user introduced plotting functionality within a simple example to showcase the model's behavior, including unstandardizing the function values and variances.
optimizationmultiobjective-optimizationbayesian-optimizationcodebasebayesian
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