Valentin Kaisermayer
Senior Researcher at BEST - Bioenergy and Sustainable Technologies GmbH
Graz, Styria, Austria
Summary
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Rockstar🎓
Top SchoolValentin Kaisermayer is a Senior Researcher and PhD-level automation and control specialist based in Graz with seven years of experience developing optimization-based energy management systems for bioenergy applications. He combines academic rigor from TU Graz with applied research at BEST, progressing from junior researcher to senior roles while contributing to projects that bridge control theory and sustainable energy. A proficient backend developer in Julia, Valentin has enhanced prominent open-source scientific libraries (Symbolics.jl, ModelingToolkit.jl, Interpolations.jl, Optimization.jl), notably adding support for array inequalities and robust interpolation modes that improve symbolic and numerical tooling. His work demonstrates a rare blend of hands-on coding for mathematical software and system-level energy optimization, making him effective at turning advanced models into practical, tested solutions. Colleagues value his attention to numerical robustness and his knack for refactoring complex systems to be more maintainable and reliable.
7 years of coding experience
6 years of employment as a software developer
Doktor (Ph.D.), Electrical Engineering, Mit Auszeichnung abgeschlossen, Doktor (Ph.D.), Electrical Engineering, Mit Auszeichnung abgeschlossen at Technische Universität GrazMatura, Mechatronics, Robotics, and Automation Engineering, 1.0, Matura, Mechatronics, Robotics, and Automation Engineering, 1.0 at PHTL Lienz
German, English
Github Skills (23)
algorithm10
symbolic-computation10
mathematics10
interpolation10
math10
mathematical-optimization10
automatic-differentiation10
differential-equations10
nonlinear-optimization10
bspline10
julia10
optimization10
testing9
grids9
indexing9
Programming languages (8)
Github contributions (5)
SciML/Optimization.jl
Jan 2022 - Dec 2022
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Role in this project:
Backend Developer
Backend DeveloperContributions:15 reviews, 14 commits, 17 PRs in 10 months
Contributions summary:Valentin primarily focused on modifying and extending the functionality of the mathematical optimization library. They updated the `nomad.jl` file to correctly handle potential `NaN` and `Inf` values, ensuring correct evaluation counting. The user refactored core functions within the project, including changing OptimizationFunction constructors and making them more explicit. They also made significant changes to handle return codes and solve times, enhancing the robustness and reporting within the project.
optimization-methodsnonlinear-optimizationautomatic-differentiationscientific-machine-learningalgorithmic-differentiation
JuliaMath/Interpolations.jl
Nov 2019 - Oct 2020
Fast, continuous interpolation of discrete datasets in Julia
Role in this project:Back-end Developer
Back-end DeveloperContributions:11 commits, 1 PR, 6 comments in 11 months
Contributions summary:Valentin primarily contributed to the `interpolations.jl` repository by adding and modifying interpolation modes within the Constant B-spline implementation. They introduced `nearest`, `next`, and `previous` interpolation modes and implemented the corresponding logic, including changes to the `positions` function and supporting functions like `floorbounds`, `ceilbounds` and `roundbounds` for correct index handling. The user also addressed test issues and merged branches to integrate their code changes. This work improved the flexibility and functionality of the interpolation library.
splinescontinuousdiscreteinterpolationjulia
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