Frederik Gerzer
Lead Software Engineer at Tensordyne
Munich, Bavaria, Germany
Summary
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Senior🎓
Top SchoolFrederik Gerzer is a Lead Software Engineer and AI specialist based in Munich with 12 years of experience bridging research and production engineering. He holds a PhD in Computer Science from TUM with a thesis on graph neural networks and has applied that expertise as an ML engineer contributing edge-pooling implementations and fixes to the widely used PyTorch Geometric library. At Tensordyne he leads engineering work that operationalizes advanced ML models, drawing on five years as a scientist at Fortiss to translate rigorous research into robust software. Frederik combines deep theoretical knowledge of graph methods with practical skills in testing and framework integration, making him adept at shipping reliable GNN functionality at scale. Colleagues describe him as a pragmatic researcher-engineer who surfaces subtle algorithmic issues early and drives them to production-ready solutions.
12 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Technical University MunichBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Karlsruhe Institute of Technology (KIT)
Github Skills (10)
geometric-deep-learning10
pytorch10
deep-learning10
graph-neural-network10
pytest10
python10
graph-convolutional-networks9
data-structure7
data-structures7
algorithms7
Programming languages (4)
Github contributions (5)
pyg-team/pytorch_geometric
Jul 2019 - Jan 2020
Graph Neural Network Library for PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 4 PRs, 12 comments in 6 months
Contributions summary:Frederik primarily contributed to the `pytorch_geometric` library, a graph neural network library. Their work focused on implementing and testing edge pooling functionality, a technique used to reduce the size of graphs. This involved writing new code for edge pooling, integrating it into the existing framework, and adding tests to ensure its functionality and robustness. Additionally, the user addressed and fixed existing issues in the codebase, demonstrating a strong understanding of the library's architecture and the mathematical concepts underlying graph neural networks.
pytorchgraph-convolutional-networksgeometric-deep-learningdeep-learningneural-graph
fgerzer/apsis
Oct 2014 - May 2017
Contributions:672 commits, 12 PRs, 163 pushes in 2 years 7 months
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