About the service
I'm a Julia language developer specializing in high-performance numerical and scientific computing. Unlike the usual workflows in Python or R, I bet on Julia's speed: I write code that runs close to native performance but stays readable and mathematically transparent, so your models don't become a bottleneck. I've built simulation engines, optimization procedures, differential equation solvers and large-scale data processing pipelines without the slowdowns typical of interpreted stacks. When you need real throughput on serious computations, Julia is the right tool, and I know how to extract maximum from it.
I start by understanding the problem: the math, the data volumes and where performance truly matters. First I profile, then I optimize, using type-stable code, multiple dispatch and correct broadcasting, relying on the ecosystem where it helps: DifferentialEquations, JuMP for optimization, DataFrames for tabular data and the SciML stack for modeling. Correctness is as important to me as speed, so I write tests, honestly measure with BenchmarkTools and document assumptions. Whether it's a prototype of a research idea or preparing a model for production — I match the engineering level to the project stage.
I can take a slow prototype in Julia, Python or MATLAB and turn it into fast, maintainable Julia code, or start from a clean technical specification. I'm comfortable with parallel computing, offloading to GPU through CUDA when justified, and packaging code so your team can actually run it. Tell me about your bottleneck and target numbers — I'll give honest assessment and a working plan.
— High-performance numerical computing and algorithm implementation in Julia
— Simulations, solving differential equations and mathematical modeling (SciML, DifferentialEquations)
— Optimization problems through JuMP and solver integration
— Data analysis and processing with DataFrames and large arrays
— Profiling, benchmarking and type-stable performance tuning
— Parallel and GPU computing, and code packaging for reuse
I start by understanding the problem: the math, the data volumes and where performance truly matters. First I profile, then I optimize, using type-stable code, multiple dispatch and correct broadcasting, relying on the ecosystem where it helps: DifferentialEquations, JuMP for optimization, DataFrames for tabular data and the SciML stack for modeling. Correctness is as important to me as speed, so I write tests, honestly measure with BenchmarkTools and document assumptions. Whether it's a prototype of a research idea or preparing a model for production — I match the engineering level to the project stage.
I can take a slow prototype in Julia, Python or MATLAB and turn it into fast, maintainable Julia code, or start from a clean technical specification. I'm comfortable with parallel computing, offloading to GPU through CUDA when justified, and packaging code so your team can actually run it. Tell me about your bottleneck and target numbers — I'll give honest assessment and a working plan.
— High-performance numerical computing and algorithm implementation in Julia
— Simulations, solving differential equations and mathematical modeling (SciML, DifferentialEquations)
— Optimization problems through JuMP and solver integration
— Data analysis and processing with DataFrames and large arrays
— Profiling, benchmarking and type-stable performance tuning
— Parallel and GPU computing, and code packaging for reuse
