Dylan Bourgeois
Founder at Stealth
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDylan Bourgeois is a French-American founder and AI engineer with 12 years of experience building embodied intelligence, robotics systems, and Spatial Generative AI for real-world automation. He has led product-critical engineering and architecture as Co-Founder & Chief Architect at Claryo and driven robot-cloud integration and no-code robot behavior tools at Robust.AI, bridging research and production. His research background includes Stanford work equating code representations to GNNs and a NeurIPS paper on GNN explanations, reflecting deep expertise in interpretability and graph methods. Dylan’s contributions span from low-level robot control and visual odometry to scalable warehouse world models, showing rare fluency across perception, planning, and systems. He actively crafts “artificially intelligent minds” in open source—improving graph data tooling—and now runs a stealth venture in New York focused on next-generation embodied agents. Colleagues would describe him as a hands-on leader who turns interpretability and common-sense reasoning research into deployable robotics and AI products.
11 years of coding experience
7 years of employment as a software developer
Master of Science & Engineering Mechatronics Robotics and Automation Engineering, Master of Science & Engineering Mechatronics Robotics and Automation Engineering at EPFLMaster's thesis Artificial Intelligence, Master's thesis Artificial Intelligence at Stanford University
French, English, Spanish
Github Skills (8)
networkx10
python10
graphing9
grapher9
graph9
data-loading9
code-completion8
codecomplete8
Programming languages (8)
Github contributions (5)
RexYing/gnn-model-explainer
Oct 2019 - Jan 2021
gnn explainer
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 54 commits, 6 PRs in 1 year 3 months
Contributions summary:Dylan primarily focused on modifying and commenting on `gengraph.py` and `load_data.py` files, which are related to generating and manipulating synthetic graphs for experiments. They fixed typos, added comments to clarify the code's functionality, and performed formatting changes to improve readability. This indicates a focus on understanding and potentially modifying the graph generation and data loading components of the project.
explainergnn
dtsbourg/ff-attention
Jun 2018 - Jun 2018
Contributions:55 commits in 12 days
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