Glenn Moynihan
Senior Research Engineer at Visa
Greater Cambridge Area United Kingdom
Summary
👤
Senior🎓
Top SchoolGlenn Moynihan is a Senior Research Engineer with a decade of experience at the intersection of machine learning, software engineering, data, and MLOps, currently contributing to research-driven teams at Featurespace and Visa. He builds composable tooling and ML platforms that enable scalable, reproducible experiments and production-ready, containerised deployments on cloud infrastructure. His background spans deep learning for neurobiomarkers with TB-scale datasets, time-series ML for electricity grid optimisation, and doctoral-level computational condensed matter research with publications in high-impact journals. An active open-source contributor, he has implemented and rigorously unit-tested probability distribution operations in the widely used JuliaStats/Distributions.jl package, reflecting a strong focus on numerical correctness and reproducibility. Known for bridging rigorous research methods with practical engineering, he routinely turns complex algorithms into maintainable, testable systems that accelerate scientific impact.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computational Condensed Matter, Doctor of Philosophy (Ph.D.) Computational Condensed Matter at Trinity College Dublin
Irish
Github Skills (6)
unit-testing10
statistics10
probability-distribution10
distributions10
data-science10
julia10
Programming languages (4)
Github contributions (5)
JuliaStats/Distributions.jl
Jun 2019 - Jul 2019
A Julia package for probability distributions and associated functions.
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 1 PR, 29 comments in 21 days
Contributions summary:Glenn's contributions primarily involve implementing and testing functions related to probability distributions within the JuliaStats/distributions.jl repository. They focused on defining the `convolve` function for various distributions, including Bernoulli, Binomial, Normal, and others, ensuring the functionality's correctness. Their work also included writing unit tests to validate the convolution operations for both univariate and multivariate distributions, confirming the results against expected values and parameter constraints. The user added and maintained unit tests to ensure proper functionality.
julia-packagedistributionsstatisticsdata-scienceprobability-distributions
glennmoy/stigler_diet
Dec 2015 - Jun 2018
Contributions:12 commits, 10 pushes, 1 branch in 2 years 6 months
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