Alberto Martín
Senior Lecturer at The Australian National University
Canberra, Australia
Summary
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Senior🎓
Top SchoolAlberto Martín is a Senior Lecturer and computational scientist with 11+ years of experience at the intersection of High Performance Scientific Computing, numerical PDEs and Scientific Machine Learning. He designs application-tailored finite element discretizations and scalable solvers, and implements them for petascale distributed-memory systems while advancing software design patterns in open-source scientific packages. His work spans numerical linear algebra, mathematical modelling, and performance-aware engineering across C/C++, Fortran, Python, Julia and workflow tools—evidenced by performance-focused contributions to the Gridap.jl library. He has a strong track record of academic and industry collaborations, turning theoretical advances into practical, reproducible software used in real-world inverse-PDE and modelling challenges. Based in Canberra and holding a PhD in Computational Science (Cum Laude), he brings both deep research rigor and hands-on optimisation experience for modern heterogeneous architectures.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Computational Science, Cum Laude, Doctor of Philosophy (PhD), Computational Science, Cum Laude at Universitat Jaume I
Spanish, English
Github Skills (7)
partial-differential-equations10
numerical-methods10
linear-algebra10
julia10
performance-optimization9
computational-geometry9
finite-element-analysis9
Programming languages (6)
Github contributions (5)
gridap/Gridap.jl
May 2020 - Jan 2023
Grid-based approximation of partial differential equations in Julia
Role in this project:
Back-end Developer & Numerical Methods Specialist
Back-end Developer & Numerical Methods SpecialistContributions:267 reviews, 337 commits, 139 PRs in 2 years 8 months
Contributions summary:Alberto made significant contributions to the `Gridap.jl` library, focusing on performance improvements and bug fixes within the `GridTopologies.jl` module. The user optimized the face-to-cells counting function and made minor typo fixes. Furthermore, they addressed merge conflicts and contributed to the algebraic operations and solution algorithms, showcasing proficiency in numerical methods for partial differential equations.
partial-differential-equationsdifferentialnumerical-integrationsplinespde
gridap/GridapHybrid.jl
May 2021 - Jul 2022
Hybrid discretisation methods in Julia (VEM, HDG, HHO, etc) :construction: :construction: :construction: Work in progress :construction: :construction::construction:
Contributions:190 commits, 19 PRs, 69 pushes in 1 year 2 months
hdgin-progressfinite-element-methodshybridjulia
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