Attila Bagoly
Chief AI Officer at Fetch.ai
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAttila Bagoly is a Chief AI Officer and veteran ML/software engineer based in Cambridge with a decade of experience bridging statistical physics, research-grade tooling, and production ML systems. He has risen through technical and leadership roles at Fetch.ai while co-founding QBio.AI, combining hands-on engineering in Go, Rust, Python and C++ with platform expertise in Kubernetes, Istio and MLOps. His PhD work in statistical physics informs a principled approach to complex modeling and uncertainty, and he has contributed to high-profile scientific software such as ROOT’s TMVA module to enable interactive, notebook-driven model diagnostics. Comfortable across backend services and frontend tooling, he ships robust pipelines and visualizations that make experimental workflows more inspectable and debuggable. Known for tackling hard problems and learning fast, he blends academic rigor with startup velocity to move models from research to scalable production.
10 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
0reached
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Github Skills (11)
root-view10
machine-learning10
c-language10
cprogramming-language10
document-root10
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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