Andreas Argyriou
Senior Data Scientist
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAndreas Argyriou is a Senior Data Scientist based in London with nine years of industry experience and a strong research background spanning UCL, KU Leuven and Ecole Centrale Paris. He currently applies advanced machine learning and back-end engineering at Microsoft, bringing a track record of turning research-grade models into reliable production systems from prior roles at KAYAK and academic posts. Andreas contributes to open-source recommender tooling—improving Python evaluation functions and stability for a prominent recommenders repository—demonstrating attention to algorithmic correctness, testing and performance tuning. He holds advanced degrees in computer science and intelligent systems (PhD, MSc) from UCL and technical training from MIT, blending deep theoretical grounding with practical engineering. Colleagues appreciate his ability to bridge rigorous research methods with production-focused software craftsmanship, often surfacing non-obvious improvements in evaluation and infrastructure.
9 years of coding experience
4 years of employment as a software developer
Massachusetts Institute of TechnologyPhD, Computer Science, PhD, Computer Science at University College London
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (7)
machine-learning10
recommendation-system10
python10
evaluation10
metric10
testing10
tensorflow6
Programming languages (5)
Github contributions (5)
recommenders-team/recommenders
Oct 2018 - Mar 2022
Best Practices on Recommendation Systems
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:444 reviews, 726 commits, 120 PRs in 3 years 6 months
Contributions summary:Andreas focused on enhancing and maintaining the Python evaluation functions. The commits reflect the addition of a new test, restructuring of the code, and the removal of a top-level interface. These changes show the user's involvement in both algorithm development and testing, which involved refactoring of the Python code to enhance efficiency within the recommendation system's environment. These edits suggest a significant involvement in the core aspects of the project, covering algorithm functionality, performance and stability.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
anargyri/lstm_han
Sep 2017 - Oct 2017
Contributions:39 commits, 1 PR, 51 pushes in 28 days
pytorchnlphierarchical-attention-networkdeep-learninglstm
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