Eleftherios Avramidis
Senior Modelling Engineer at Arm
Cambridge, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolEleftherios Avramidis is a Senior Modelling Engineer based in Cambridge with 11 years of experience applying mathematical optimisation, HPC and GPGPU techniques to research and industrial problems. He holds a PhD in Mathematical Optimisation and GPGPU from the University of Exeter and has a strong track record of porting and optimising scientific codes across heterogeneous hardware, from Movidius Myriad accelerators to modern GPUs. At Arm and the University of Cambridge he led efforts to modernise, refactor and GPU-enable legacy simulation and astrophysics codes, and contributed performance and correctness fixes to the widely used Shogun machine learning toolbox. Comfortable in C++, Python and Fortran, he combines deep numerical insight with pragmatic engineering—often replacing high-level library components with LAPACK-level optimisations to squeeze out performance. Colleagues rely on him for turning complex research models into production-ready, high-performance implementations.
11 years of coding experience
5 years of employment as a software developer
Master of Science - MSc, Cognitive Science, Master of Science - MSc, Cognitive Science at The University of EdinburghBachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at ATEI ThessalonikiDoctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at University of Exeter
Github Skills (8)
machine-learning10
c-language10
cprogramming-language10
cmake9
eigen8
data-science8
swig7
artificial-intelligence7
Programming languages (4)
Github contributions (5)
shogun-toolbox/shogun
Nov 2018 - Mar 2019
Shōgun
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 9 PRs, 95 comments in 3 months
Contributions summary:Eleftherios primarily contributed to the Shogun machine learning toolbox by fixing bugs, adding benchmarks, and updating meta examples. They addressed initialization issues in copy constructors within C++ code, specifically for string features. Additionally, they fixed compilation errors related to gcc8 and optimized the code by replacing Eigen's symmetric eigensolver with LAPACK. Finally, they added and converted meta examples for different features within the Shogun framework.
cmakedata-sciencegunc-plus-plusmachine-learning
avramidis/chronos
Sep 2019 - Dec 2020
Contributions:2 PRs, 65 pushes, 8 branches in 1 year 3 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.