Marc Brockschmidt
Senior Staff Research Scientist at Google DeepMind
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMarc Brockschmidt is a Senior Staff Research Scientist at DeepMind with 12 years of experience applying deep learning to highly structured data such as programs, graphs, and molecules. He has led research and engineering efforts at Microsoft and DeepMind, focusing on teaching machines to assist software developers and advancing program analysis through ML-driven program synthesis, verification, and bug finding. His work on graph neural networks is practical and performance-oriented—contributing optimizations, new architectures (e.g., edge-MLP R-GCN variants), and sparse-GNN speedups to notable Microsoft GNN sample repos. Trained as a PhD computer scientist from RWTH Aachen, he blends formal program-analysis expertise (termination and complexity analysis) with hands-on ML engineering. Colleagues describe him as someone who moves ideas from provable theory into production-ready models and tooling. He often surfaces non-obvious gains by combining classical program-verification insights with modern neural architectures to improve robustness and interpretability.
12 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at RWTH Aachen University
German, English
Github Skills (15)
model-building10
machine-learning10
tensorflow10
graph-neural-network10
performance-optimization10
python10
modeling10
model-driven10
model-driven-development10
trainings9
subsampling8
benchmark8
benchmarking8
resampling8
sampling8
Programming languages (10)
Github contributions (5)
microsoft/gated-graph-neural-network-samples
Nov 2017 - Sep 2019
Sample Code for Gated Graph Neural Networks
Role in this project:
ML Engineer
ML EngineerContributions:34 commits, 7 PRs, 18 pushes in 1 year 10 months
Contributions summary:Marc primarily focused on optimizing and enhancing the performance of Gated Graph Neural Network models within the repository. Their contributions include refactoring code, adding new functionalities like residual connections and edge-wise attention, and implementing various optimizations to improve the speed and efficiency of the models, especially related to sparse GNNs. The user also introduced several configurable parameters to the model, allowing for greater flexibility, including allowing the user to simulate graph convolutional networks. Furthermore, the user integrated features for saving, restoring, and sampling examples for several tasks.
dataminingneural-graphneural-networksgraph-neural-networkgraph-neural-networks
microsoft/tf-gnn-samples
Jun 2019 - Apr 2021
TensorFlow implementations of Graph Neural Networks
Role in this project:ML Engineer
ML EngineerContributions:34 commits, 5 PRs, 16 pushes in 1 year 9 months
Contributions summary:Marc primarily contributed to implementing and improving Graph Neural Network (GNN) models within the TensorFlow framework. They added new GNN variants, specifically an edge-MLP based R-GCN model and made improvements to existing models like the GIN. Furthermore, the user worked on optimizing the training process by adding features such as normalized learning rates and better logging for multiple runs. They also contributed to the benchmark scripts and documented the results.
implementationsdeep-learningneural-graphneural-networksgraph-neural-networks
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