Benedek Rozemberczki
Staff Research Scientist at Google
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolBenedek Rozemberczki is a Staff Research Scientist with a decade of experience building and shipping graph machine learning systems, currently at Google after a research-focused tenure at Isomorphic Labs. He holds a PhD in Data Science from the University of Edinburgh and combines deep academic grounding with hands‑on PyTorch engineering—authoring and contributing to influential open-source graph libraries such as PyTorch Geometric and Karate Club. His work spans graph representation learning, spatio-temporal GNNs, and scalable training algorithms (Cluster-GCN, SimGNN, graph2vec), reflecting both model-building and dataset engineering expertise. Comfortable moving ideas from papers to production, he has implemented canonical benchmarks and novel datasets (e.g., GitHub, Twitch, Pathfinder) that broaden real-world GNN evaluation. Based in London, he brings a rare mix of research rigor, practical coding impact, and domain breadth informed by economics and data science training.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Data Science, Doctor of Philosophy - PhD, Data Science at The University of EdinburghMaster of Arts (M.A.), Economic Policy, Master of Arts (M.A.), Economic Policy at Central European UniversityBachelor of Arts (B.A.), Applied Economics, Bachelor of Arts (B.A.), Applied Economics at Corvinus University of Budapest
English, Hungarian
Github Skills (21)
geometric-deep-learning10
pytorch10
lib10
python10
gnn10
data-set10
clustering-algorithm10
attention-mechanism10
machine-learning10
networkx10
datasets10
graph-embedding10
deeplearning-ai10
deep-learning10
graph-neural-network10
Programming languages (12)
Github contributions (5)
benedekrozemberczki/CapsGNN
Jan 2019 - Nov 2022
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 177 commits, 3 PRs in 3 years 9 months
Contributions summary:Benedek contributed significantly to the implementation and refinement of a Capsule Graph Neural Network (CapsGNN) model. Their commits encompass core model architecture definition, including the integration of GCN layers, primary and secondary capsule layers, and an attention mechanism. The user was also involved in defining and integrating the margin loss function, crucial for training the capsule networks, as well as integrating the histogram reconstruction layers and their corresponding loss calculations.
sklearnconvolutionstruc2veccapsule-networktensorflow
benedekrozemberczki/karateclub
Dec 2019 - Jan 2023
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Role in this project:Back-end Developer
Back-end DeveloperContributions:105 releases, 2233 commits, 35 PRs in 3 years 2 months
Contributions summary:Benedek primarily focused on adding and modifying classes and import statements related to the "KarateClub" library. Their contributions involved implementing components for both overlapping and non-overlapping clustering algorithms, including class definitions for Overlapping, NonOverlapping, and initialization files. Furthermore, the user updated setup files to reflect version updates.
node-embeddingpythonsklearngraph-clusteringpython-framework
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