Javier F
Lead Research Scientist at Flower Labs
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJavier F is a lead research scientist specializing in federated and on-device machine learning, currently driving the Flower framework at Flower Labs and collaborating as a visiting researcher with the Machine Learning Systems Group at the University of Cambridge. With an 11-year track record spanning industry and academia, he holds a DPhil from Oxford on high-performance deep learning for resource-constrained platforms and previously advanced hardware-aware ML at Samsung AI Cambridge. He combines systems-level engineering (C++, mobile optimizations, Winograd-aware CNN transformations) with practical ML research, contributing to the popular open-source Flower federated learning framework and embedded-device examples for CIFAR-10. Javier supervises undergraduate and MPhil projects, bridging cutting-edge research with real-world deployments, and is known for squeezing latency and quantization gains out of constrained hardware without sacrificing accuracy.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of OxfordBachelor's Degree Telecommunications System Engineering, Bachelor's Degree Telecommunications System Engineering at University of NavarraMaster’s Degree Computer Vision, Master’s Degree Computer Vision at Queen Mary University of London
Spanish, English, French, German
Github Skills (7)
mask-rcnn10
faster-rcnn10
pytorch10
image-classification9
machine-learning9
simulations9
simulation9
Programming languages (4)
Github contributions (5)
adap/flower
Dec 2020 - Apr 2022
Flower: A Friendly Federated AI Framework
Role in this project:
ML Engineer
ML EngineerContributions:1496 reviews, 17 commits, 792 PRs in 1 year 4 months
Contributions summary:Javier contributed to the development of an example for embedded devices, by adding code differences to the client.py and utils.py files, and focused on image classification using PyTorch for CIFAR-10. They implemented, or demonstrated their knowledge of a CNN architecture. Additional commits show updates to the examples, with the goal of upgrading different technologies involved, such as `Ray`, as well as updating simulation examples.
federated-analyticsfederated-learning-frameworkmachine-learningkeras-federated-learningflower
jafermarq/flower
Nov 2020 - Mar 2025
Flower - A Friendly Federated Learning Research Framework
Contributions:8 reviews, 18 PRs, 168 pushes in 4 years 4 months
pytorchdeep-learningmachine-learningflowerfederated-learning
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