Julian Mcginnis
Doctoral Student at Freelance
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolJulian Mcginnis is a doctoral student and machine learning engineer based in Munich with eight years of experience bridging embedded systems and graph ML research. He applies deep technical skills—from C/C++ and PCB design for startups to PyTorch-based GCN and GraphSAGE implementations for link prediction on well-known graph benchmarks—to both academic and product-focused projects. Currently researching implicit representations and applied ML for multiple sclerosis at Klinikum rechts der Isar, he pairs rigorous M.Sc. training from TUM with practical industry experience at RAFI and Fraunhofer. As a freelancer he helps teams turn prototypes into production hardware-software systems, and his open-source contributions reflect a rare fluency across low-level hardware and state-of-the-art graph neural networks.
8 years of coding experience
2 years of employment as a software developer
Master of Science (M.Sc.), Electrical Engineering and Information Technology, 1,1, Master of Science (M.Sc.), Electrical Engineering and Information Technology, 1,1 at Technical University MunichBachelor of Engineering (B.Eng.), Electrical Engineering, Communications Engineering, 1,6, Bachelor of Engineering (B.Eng.), Electrical Engineering, Communications Engineering, 1,6 at Duale Hochschule Baden-Württemberg
Github Skills (9)
graphdb10
pytorch10
deep-learning10
graph-neural-network10
graphml10
machine-learning-models10
matrix-factorization9
datasets8
python8
Programming languages (4)
Github contributions (5)
snap-stanford/ogb
Aug 2022 - Aug 2022
Benchmark datasets, data loaders, and evaluators for graph machine learning
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 25 commits, 5 PRs in 20 days
Contributions summary:Julian contributed to the development of machine learning models for link prediction within the graph machine learning domain. They implemented both Node2Vec and Matrix Factorization (MF) algorithms, adding related components such as a logger and demonstrating knowledge of PyTorch and graph datasets. Furthermore, they worked on an initial draft of an MLP model and a GNN-based architecture, demonstrating an understanding of diverse modeling approaches for link prediction. They are also the one responsible for the GCN and GraphSAGE implementation.
loadersdeep-learningmachine-learningbenchmarkgraph-neural-networks
jqmcginnis/spinalcordtoolbox
Jun 2022 - Nov 2024
Comprehensive and open-source library of analysis tools for MRI of the spinal cord.
Contributions:40 pushes, 13 branches in 2 years 5 months
neuroimagingcomputed-tomographyvolume-renderingregistrationanalysis-tools
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