Dimitris Stripelis
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolDimitris Stripelis is a machine learning and distributed systems engineer with 11 years of experience focused on federated learning, ML systems, and data management. He is the creator of the Metis Federated Learning (MetisFL) framework and contributes to the wider federated AI ecosystem through documentation and usability improvements for projects like the widely-used Flower framework. Based in the San Francisco Bay Area, he combines rigorous academic training—a PhD and MS in Computer Science from USC and a BS from Athens University of Economics and Business—with hands-on systems engineering. His work emphasizes making complex, privacy-preserving ML workflows reproducible and accessible, from aggregation strategies to cloud deployment how-tos. A detail-oriented collaborator, he improves developer experience by clarifying tutorials and upgrading guides that lower the barrier to production for federated AI.
11 years of coding experience
Bachelor of Science (BS), Computer Science, 7.94, Bachelor of Science (BS), Computer Science, 7.94 at Athens University of Economics and BusinessMaster's Degree, Computer Science - Data Science, 3.87, Master's Degree, Computer Science - Data Science, 3.87 at University of Southern California
French, Greek, English
Github Skills (8)
user-manual10
learn-ruby-on-rails10
basics10
documentation10
python5
microsoft-azure5
azure5
xgboost4
Programming languages (6)
Github contributions (5)
adap/flower
Apr 2024 - Mar 2025
Flower: A Friendly Federated AI Framework
Role in this project:
Technical Writer
Technical WriterContributions:80 reviews, 13 PRs, 30 pushes in 11 months
Contributions summary:Dimitris primarily contributed to the documentation within the Flower framework repository. They fixed typos in the tutorial documentation, updated quickstart examples related to XGBoost, and revised documentation on aggregating evaluation results. Furthermore, the user updated the documentation on how to upgrade to Flower 1.13 and added a how-to guide for running Flower on Microsoft Azure VM instances. These contributions significantly improve the clarity and usability of the framework for new and existing users.
federated-analyticsfederated-learning-frameworkmachine-learningkeras-federated-learningflower
dstripelis/FedSparsify
Sep 2021 - May 2023
Contributions:29 commits, 4 PRs, 12 pushes in 1 year 7 months
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