Emmanouil Michalainas
Quantitative Developer
Dublin, Ireland
Summary
🤩
Rockstar🎓
Top SchoolEmmanouil Michalainas is a Quantitative Developer based in Dublin with six years of experience at the intersection of high-performance computing and finance. He has driven production-grade performance improvements—10-50x speedups—in CERN's RooFit toolkit through auto-vectorization, multithreading and CUDA, and contributed backend optimizations to the widely used ROOT project. At SIG he applies that low-level performance expertise to quantitative systems, and as former Lead Developer of a crypto fintech he has end-to-end product and infrastructure experience. He teaches algorithmic thinking too, having coached students to national and international programming competitions. Emmanouil combines algorithmic rigor and systems-level optimization with hands-on trading technology delivery, and he often uncovers wins by refactoring compute-heavy routines rather than adding hardware.
6 years of coding experience
1 year of employment as a software developer
Master of Engineering - MEng, Electrical and Computer Engineering, 8.91 / 10, Master of Engineering - MEng, Electrical and Computer Engineering, 8.91 / 10 at Aristotle University of Thessaloniki (AUTH)
Github Skills (10)
statistics10
root-view10
c-language10
cpp10
cprogramming-language10
document-root10
cplus10
data-analysis10
python9
machine-learning9
Programming languages (3)
Github contributions (5)
root-project/root
Jul 2019 - Oct 2021
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Role in this project:
Back-end Developer
Back-end DeveloperContributions:28 reviews, 48 commits, 40 PRs in 2 years 3 months
Contributions summary:Emmanouil's contributions primarily involve implementing and optimizing features within the RooFit framework. Their work centers on the `RooFit` library, focusing on adding functionality to the `fitTo()` method and enhancing the capabilities of the RooFitCompute library. The user introduced a `PrefitDataFraction` parameter for the `fitTo()` method to speed up fitting processes and also added doxygen documentation. Additionally, the user transferred vectorised computation functions to the Compute Library, refactoring and enhancing overall computational performance.
pythonroot-cernmathematicsc-plus-plusscientific-visualization
manolismih/root
Jul 2019 - Nov 2021
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Contributions:2 PRs, 121 pushes, 41 branches in 2 years 4 months
analyzingdata-analysispythondata-sciencestoring
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