Attila Bagoly
Chief AI Officer at Fetch.ai
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAttila Bagoly is an ML and software engineering leader based in Cambridge with nine years of experience bridging physics research and production-grade AI systems. He serves as Chief AI Officer at Fetch.ai while co‑founding and acting as CTO of QBio.AI, a startup applying AI and quantum methods to drug discovery. Holding a PhD in statistical physics/AI, he is a polyglot engineer comfortable across Go, Python, Rust, C/C++, TypeScript/React and cloud-native MLOps (Kubernetes, Helm, Istio). Notably, he has contributed to ROOT’s TMVA module to enable interactive Jupyter training visualizations and more robust DataLoader analytics — an example of his focus on making advanced research tooling practical for developers and scientists.
9 years of coding experience
11 years of employment as a software developer
ELTE Bolyai CollegeDoctor of Philosophy - PhD Statistical physics/Artificial intelligence, Doctor of Philosophy - PhD Statistical physics/Artificial intelligence at Eötvös Loránd University
Hungarian, English, Romanian
Stackoverflow
1reputation
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Github Skills (11)
root-view10
machine-learning10
c-language10
document-root10
c-programming-language10
data-analysis10
visualizations9
jupyter-notebook9
visualization9
statistics9
python8
Programming languages (8)
Github contributions (5)
root-project/root
Jul 2016 - Oct 2016
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Role in this project:
Back-end Developer
Back-end DeveloperContributions:25 commits, 6 PRs in 2 months
Contributions summary:Attila contributed to the TMVA (Toolkit for Multivariate Analysis) module within the ROOT project, specifically focusing on enhancements to the `DataLoader` class and the integration of an interactive training environment within a Jupyter notebook. They implemented the `GetCorrelationMatrix` function in the `DataLoader` and enabled interactive training visualization, including error graphs, for various machine learning methods like MLP, DNN, and BDT. These changes provide users with a more interactive and visual way to monitor and debug their machine learning models.
pythonroot-cernmathematicsc-plus-plusscientific-visualization
qati/ledger
Nov 2019 - Jan 2020
Official Fetch.AI Ledger C++ implementation
Contributions:1 PR, 83 pushes, 13 branches in 2 months
cppc-plus-plusfetch-aicpp17ledger
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