Nikola Stoyanov
Staff Engineer at Arm
United Kingdom
Summary
👤
Senior🎓
Top SchoolNikola Stoyanov is a Staff Engineer with 8 years of experience blending research and production-grade software development across companies like IBM Research and Arm. He brings deep numerical and backend expertise, evidenced by contributions to SciML’s widely used OrdinaryDiffEq.jl where he implemented and validated advanced ODE solvers such as SDIRK22. Comfortable moving between research settings and industry engineering, Nikola has a track record of shipping core algorithmic changes and performance-minded implementations. Based in the UK with an MEng from The University of Manchester and a PhD background, he combines academic rigor with practical systems experience. Colleagues rely on him for solving tricky numerical problems and integrating them into robust backend systems.
8 years of coding experience
11 years of employment as a software developer
Master of Engineering (MEng) Civil Engineering with Industrial Experience Civil and Structural Engineering, Master of Engineering (MEng) Civil Engineering with Industrial Experience Civil and Structural Engineering at The University of ManchesterMath High School "Acad. Kiril Popov"
Stackoverflow
1reputation
0reached
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Github Skills (10)
algorithm10
data-structures10
algorithms10
ode10
numerical-methods10
ordinary-differential-equations10
data-structure10
julia10
high-performance9
test-automation8
Programming languages (5)
Github contributions (5)
SciML/OrdinaryDiffEq.jl
Aug 2019 - Aug 2019
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Role in this project:
Back-end Developer / Algorithm Implementer
Back-end Developer / Algorithm ImplementerContributions:6 commits, 1 PR, 6 comments in 13 days
Contributions summary:Nikola focused on extending the `ordinarydiffeq.jl` library by implementing and testing new Ordinary Differential Equation (ODE) solvers. Their work included adding the SDIRK22 algorithm, integrating it into the solver framework, and developing convergence tests to validate its correctness. The user also made modifications to existing algorithms and caches, demonstrating a deep understanding of the library's internal workings and the numerical methods employed. This involved significant changes to core files related to algorithm implementation and step calculations.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
NikStoyanov/dotfiles
Nov 2018 - Mar 2025
Contributions:128 pushes, 1 branch, 1 issue in 6 years 5 months
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