Javier Tordera
Senior Data Scientist
Barcelona, Catalonia, Germany
Summary
👤
Senior🎓
Top SchoolJavier Tordera is a Senior Data Scientist and biomedical engineer with a PhD in computational medical imaging, combining six years of hands-on research and production experience in MRI acquisition, reconstruction and AI-driven image analysis. Currently at Bayer after a Research Fellowship at UCL, he builds deployable ML pipelines, MR pulse sequences and clinical integrations used at NHS sites and in collaboration with Siemens Healthineers. He authors and maintains TensorFlow MRI and TensorFlow NUFFT and has contributed performance-critical GPU work to Gadgetron, reflecting deep expertise in TensorFlow, CUDA and MR reconstruction. Javier emphasizes software engineering best practices—testing, CI and maintainable code—to translate state-of-the-art methods into reliable clinical tools. He is particularly skilled at bridging academic research and real-world deployment, having placed models on scanners and radiology workstations for multi-centre studies.
6 years of coding experience
4 years of employment as a software developer
Universitat Politècnica de ValènciaMaster of Science - MS Biomedical Engineering, Master of Science - MS Biomedical Engineering at Imperial College LondonUniversity College London
Spanish, English, Catalan
Github Skills (9)
cuda10
gpu-programming10
c-language10
cprogramming-language10
mri10
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (5)
Github contributions (5)
gadgetron/gadgetron
May 2020 - Sep 2020
Gadgetron - Medical Image Reconstruction Framework
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:11 commits, 5 PRs, 9 comments in 4 months
Contributions summary:Javier primarily contributed to optimizing the Gadgetron framework by adding GPU support for 3D CSM estimation. Their work involved modifying CUDA kernels to smooth correlation matrices for improved image reconstruction, indicating a focus on performance enhancement. They also added density compensation for arbitrary trajectories, improving the framework's ability to handle diverse data acquisition schemes. Furthermore, the user fixed a bug in a core function, ensuring the correct behavior of the software.
reconstructionimage-reconstructionmedical-imagedeep-learningcomputer-vision
mrphys/tensorflow-mri
Jul 2021 - Oct 2022
A Library of TensorFlow Operators for Computational MRI
Contributions:1 release, 4 reviews, 581 commits in 1 year 3 months
pythondeep-learninginverse-problemsmrimachine-learning
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