Alejandro Sierra
Docent Quantitative Crop Ecology
Wageningen, Gelderland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolAlejandro Sierra is a Docent in Quantitative Crop Ecology at Wageningen with 12 years of experience applying computational modelling and data analysis to plant and agroecosystem problems. He holds a PhD in Plant Sciences and has led research on crop responses to drought, bridging field measurements with mechanistic models since his PhD on rice adaptation. Alejandro combines domain expertise in carbon and water fluxes with strong software craftsmanship, contributing backend enhancements to the widely used SciML ModelingToolkit.jl—adding symbolic-computation macros and variable-extraction algorithms that improve automated equation handling. His work spans academia and open source, enabling reproducible, physics-informed modelling workflows for agricultural research. Colleagues value him for translating complex ecological processes into scalable, well-tested code that informs both experiments and decision-making.
12 years of coding experience
4 years of employment as a software developer
MSc Degree, Crop Science, MSc Degree, Crop Science at Wageningen UniversityEngineer (BSc), Forestry, Engineer (BSc), Forestry at University of CordobaPhD, Plant Sciences, PhD, Plant Sciences at Wageningen University & Research
Spanish, French, English
Github Skills (8)
symbolic-computation10
computer-algebra10
differential-equations10
modeling10
julia10
scientific-machine-learning9
macros9
optimization7
Programming languages (4)
Github contributions (5)
SciML/ModelingToolkit.jl
Mar 2018 - Mar 2018
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 4 PRs, 1 push in 17 days
Contributions summary:Alejandro focused on extending the `modelingtoolkit.jl` library, primarily by implementing macros and functions related to symbolic computation. They added features to register functions and operators, handle literals, and generate methods for various argument combinations. The user also refactored variable construction and implemented algorithms to extract variables from equations, contributing to the core functionality of the symbolic modeling framework. These changes enabled automatic extraction of parameters and variables within the library.
computer-algebra-systemjuliamachine-learningscientific-machine-learningdifferential-equations
AleMorales/ODEDSL.jl
Feb 2015 - Aug 2019
Contributions:32 pushes, 1 branch in 4 years 6 months
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