Javier Turek
Scientist Building The Future Of Applied AI at Earth Dynamics AI
Beaverton, Oregon, United States
Summary
👤
Senior🎓
Top SchoolJavier Turek is a machine learning researcher and applied AI scientist with a PhD from Technion and over a decade of experience translating cutting-edge research into production-ready systems. He has led ML teams at Intel Labs—advancing long-context language models, creating a power-law forget gate for LSTMs, and delivering sim-to-real vision pipelines—and now builds applied AI products at Earth Dynamics AI. Javier’s work spans neuroscience-grade fMRI analysis and industrial computer vision, including deploying a compressed-sensing MRI pipeline in a pediatric hospital and accelerating visual inspection from 500ms to 20ms for manufacturing. An active contributor to the brainiak open-source project, he implemented and optimized the Shared Response Model and added semi-supervised and robust components integrated with scikit-learn. He combines deep theoretical insight with hands-on engineering—designing camera rigs, distributed training workflows, and reproducible ML methods that reduce random-seed variability—making him adept at bridging research, engineering, and real-world impact.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Technion - Israel Institute of Technology
Spanish, English, Hebrew
Github Skills (11)
scikit-learn10
scikit10
algorithm10
neuroscience10
algorithms10
machine-learning10
python10
implement10
mri10
data-analysis9
distributed-computing7
Programming languages (5)
Github contributions (5)
brainiak/brainiak
Mar 2016 - Feb 2021
Brain Imaging Analysis Kit
Role in this project:
Data Scientist
Data ScientistContributions:30 commits, 11 PRs, 1 branch in 4 years 11 months
Contributions summary:Javier implemented and refined the Shared Response Model (SRM) within the `brainiak/brainiak` repository, including both probabilistic and deterministic variants. Their contributions focused on algorithm implementation, optimization, and integration with scikit-learn's BaseEstimator. They also added unit tests and example code. Additionally, the user incorporated features like semi-supervised learning and robust components.
brainconnectivityneuroimagingneurosciencepython
IntelPNI/pni-hackathon
Jul 2016 - Jul 2023
Contributions:3 pushes in 7 years
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