Gianluca Campanella
Lead Staff Data Scientist
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolGianluca Campanella is a Lead Staff Data Scientist based in London with nine years of experience building data-driven products and leading teams across advertising, finance, and research. He blends academic rigour from a PhD in Biostatistics and fellowships at Imperial College with hands-on industry leadership at Visa and The Trade Desk, delivering production ML solutions on cloud platforms. His open-source contributions include extending autograd to support gradients for scipy.stats distributions, reflecting deep expertise in automatic differentiation and probabilistic modeling. He has a strong teaching and mentoring pedigree—supervising MSc students and training over 500 data practitioners through consultancy and faculty roles. Comfortable bridging research and engineering, he optimises distributed systems too, having tuned Spark configurations for recommendation-system testing. Colleagues describe him as a practical scientist who turns complex statistical methods into robust, scalable implementations.
9 years of coding experience
10 years of employment as a software developer
Master of Science - MS, Applied Mathematics, Master of Science - MS, Applied Mathematics at Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa - FCT NOVADoctor of Philosophy - PhD, Biostatistics, Doctor of Philosophy - PhD, Biostatistics at Imperial College LondonBachelor of Science - BS, Applied Computer Science, Bachelor of Science - BS, Applied Computer Science at Free University of Bozen-Bolzano
Italian, English, Portuguese
Stackoverflow
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0reached
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Github Skills (18)
scipy10
spark10
pytest10
python10
distributions10
numpy10
automatic-differentiation10
autograd10
probability-distribution10
testing9
unit-testing9
machine-learning9
cicd8
data-science8
kubernetes4
Programming languages (5)
Github contributions (5)
HIPS/autograd
Oct 2017 - Nov 2017
Efficiently computes derivatives of NumPy code.
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 5 PRs, 9 comments in 16 days
Contributions summary:Gianluca contributed significantly to the `autograd` repository, focusing on implementing and testing the automatic differentiation of statistical functions from `scipy.stats`. They implemented and refined VJPs (vector-Jacobian products) for several probability distributions, including Poisson, Gamma, Beta and Chi-squared functions, allowing for gradient calculations in the autograd framework. This involved modifications to existing code, adding new functionalities, and writing comprehensive unit tests to ensure accuracy and correctness of the gradient calculations.
pythonautomatic-differentiationnumpyautodiffderivatives
recommenders-team/recommenders
Mar 2019 - Mar 2019
Best Practices on Recommendation Systems
Role in this project:DevOps Engineer
DevOps EngineerContributions:6 commits, 1 PR, 1 push in 1 day
Contributions summary:Gianluca primarily focused on configuring and updating Spark settings within the testing environment. Their commits reveal modifications to the `conftest.py` file, specifically adjusting Spark configuration parameters related to memory allocation, core usage, and network timeouts. These changes indicate an effort to optimize Spark performance and resource utilization for testing purposes. They also fixed docstrings within the tests.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
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