Yiannis Simillides
Senior Quantitative Analyst
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolYiannis Simillides is a Senior Quantitative Analyst based in London with nine years of experience bridging computational mathematics, machine learning and production analytics. He holds a PhD from Imperial College and has a strong engineering pedigree from roles at The Alan Turing Institute and contributions to Julia ML tooling, where he focused on robust test automation for the MLJ.jl ecosystem. Yiannis has taught and designed graduate courses on Julia, translating research-grade numerical methods into practical, high-performance code and curricula. In industry he has moved from research and teaching into quantitative roles at Cheniere Energy and software engineering at ComplyAdvantage, demonstrating an ability to deliver both rigorous models and production-quality systems. Notably, his background in testing and API design for Julia machine-learning libraries signals an attention to reliability that complements his quantitative modelling strengths.
9 years of coding experience
4 years of employment as a software developer
High School, High School at Institute of Mathematics and ScienceHigh School, Mathematics, Greek, Physics, Computing, High School, Mathematics, Greek, Physics, Computing at Lyceum GeroskipouDoctor of Philosophy - PhD, Computational Mathematics and Mechanical Engineering, Doctor of Philosophy - PhD, Computational Mathematics and Mechanical Engineering at Imperial College LondonUniversity College London
English, Greek
Stackoverflow
1reputation
0reached
0answers
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Github Skills (11)
testing10
machine-learning10
julia10
regression8
classification8
ensemble-learning7
pipeline7
auto-tuning6
performance-tuning6
data-science6
fine-tuning6
Programming languages (6)
Github contributions (5)
JuliaAI/MLJ.jl
Nov 2018 - May 2019
A Julia machine learning framework
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:87 commits, 15 PRs, 813 pushes in 6 months
Contributions summary:Yiannis primarily contributed to the testing framework of the MLJ.jl project. Their commits focused on updating and adding test cases for various components, including metrics, datasets, KNN, decision trees, transformers, and trainable models. They added tests for a new XGBoost interface and updated the runtests.jl file to include these new tests. The changes reflect a focus on ensuring the reliability and functionality of the machine learning library through comprehensive testing.
framework-learningensemble-learningclassificationstatisticspipeline
ysimillides/Introduction-to-Julia
Jun 2021 - Nov 2021
Contributions:15 commits, 1 PR, 13 pushes in 4 months
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