James Hensman
Senior Principal Researcher at Microsoft
Cambridge, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolJames Hensman is a Senior Principal Researcher based in Cambridge with 16 years of experience applying probabilistic machine learning and Gaussian process methods across both industry and academia. He has held senior research roles at Microsoft and Amazon and led modelling teams at Secondmind and PROWLER.io, translating advanced Bayesian techniques into production-ready solutions. His open-source contributions to the influential GPy and GPyOpt libraries show deep hands-on expertise in Gaussian process modelling, Bayesian optimization, and numerics—improving robustness, sparse GP estimation, and visual diagnostics. Trained as a PhD engineer from the University of Sheffield, he combines rigorous academic foundations with product-focused research leadership and a track record of shipping ML systems at scale. An understated strength is his ability to bridge reproducible research and engineering pragmatism, smoothing the path from demos and papers to deployable algorithms.
16 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Mechanical Engineering, Doctor of Philosophy - PhD, Mechanical Engineering at The University of Sheffield
Stackoverflow
21reputation
6kreached
1answer
0questions
Github Skills (22)
python10
machine-learning10
gaussian-processes10
gpy10
bayesian10
numerical-analysis10
linear-algebra10
scipy9
numpy9
mathematical9
optimisation9
modeling9
optimization9
scikit8
scikit-learn8
Programming languages (4)
Github contributions (5)
SheffieldML/GPy
Nov 2012 - Oct 2015
Gaussian processes framework in python
Role in this project:
Data Scientist
Data ScientistContributions:942 commits, 11 PRs, 79 pushes in 2 years 11 months
Contributions summary:James contributed to the GPy framework, specifically by modifying the regression demos and the behavior of the checkgrad function. They also fixed issues in the multiple optima demo and made changes to the linear kernel, as well as fixing a minor issue in the plots. Their work appears to involve debugging and improving the robustness of the Gaussian process models and demonstrating the library's capabilities.
gaussiangaussian-processespython
SheffieldML/GPyOpt
Aug 2014 - Nov 2014
Gaussian Process Optimization using GPy
Role in this project:ML Engineer
ML EngineerContributions:11 commits, 1 comment in 3 months
Contributions summary:James primarily contributed to the core Bayesian Optimization (BO) functionalities within the `gpyopt` repository, modifying existing code to work with updated GPy and adjusting optimization settings. They implemented changes related to sparse Gaussian Process (GP) estimation and optimization parameters, demonstrating a focus on improving model performance and flexibility. Their work also included updates to plotting functions, enhancing the visualization capabilities of the optimization process, and ensuring the optimization ran correctly in the case of numerical errors.
pythongpyoptimizationgaussiangaussian-process
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