Vladimir Ilievski
Senior Data Scientist
Zürich Metropolitan Area Switzerland
Summary
👤
Senior🎓
Top SchoolVladimir Ilievski is a Senior Data Scientist based in the Zürich area with 11 years of experience building production-ready ML systems and research-grade analytics. He currently leads data science efforts at Frontiers while also lecturing in data science and creating accessible ML content as the author of iSquared. His background spans industry and research, including contributions at CERN and a Master’s from EPFL, and hands-on roles from ML engineering at iGenius to internships at Swisscom and PMI. Vladimir is an active open-source contributor—he implemented convolutional backends for CNNs in the widely used ROOT project's TMVA module, demonstrating deep algorithmic and mathematical expertise. He combines practical product delivery with a flair for explaining complex concepts to broader audiences. Colleagues describe him as a pragmatic scientist who moves smoothly between low-level model implementations and clear, engaging technical communication.
11 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Bachelor’s Degree at Faculty of Computer Science and Engineering - SkopjeMaster’s Degree, Master’s Degree at EPFL (École polytechnique fédérale de Lausanne)
English, German, French, Macedonian
Github Skills (15)
algorithm10
mask-rcnn10
data-structures10
faster-rcnn10
algorithms10
machine-learning10
c-language10
cprogramming-language10
data-structure10
data-analysis10
geometry9
parallel8
root-view8
document-root8
tensorflow8
Programming languages (6)
Github contributions (5)
root-project/root
Jul 2017 - Aug 2017
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:64 commits, 5 PRs, 1 comment in 1 month
Contributions summary:Vladimir implemented the back-end for Convolutional Neural Networks (CNNs) within the ROOT project's TMVA (Toolkit for Multivariate Analysis) module. Their primary focus was on adding propagation backends for Conv and Max Pool layers, crucial components for the reference architecture. The contributions include forward and backward passes along with their specific implementations related to convolution, including Im2col, RotateWeights, and bias calculations, showcasing significant contributions in the area of machine learning algorithms and associated mathematical calculations.
pythonroot-cernmathematicsc-plus-plusscientific-visualization
tmvadnn/root
Jul 2017 - Aug 2017
Fork of ROOT Repository for GSoC'17 project in Deep Neural Networks
Contributions:63 commits, 1 PR, 30 pushes in 1 month
caffe2deep-learninggsocconvolutional-neural-networkneural-networks
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.